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Phosphorus (P) availability affects the response of terrestrial ecosystems to environmental and climate change (e.g., elevated CO2), yet the magnitude of this effect remains uncertain. This uncertainty arises mainly from a lack of quantitative understanding of the soil biological and geochemical P cycling processes, particularly the P exchange with soil mineral surfaces, which is often described by a Langmuir sorption isotherm. We first conducted a literature review on P sorption experiments and terrestrial biosphere models (TBMs) using a Langmuir isotherm. We then developed a new algorithm to describe the inorganic P exchange between soil solution and soil matrix based on the double-surface Langmuir isotherm and extracted empirical equations to calculate the sorption capacity and Langmuir coefficient. We finally tested the conventional and new models of P sorption at five beech forest sites in Germany along a soil P stock gradient using the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. We found that the conventional (single-surface) Langmuir isotherm approach in most TBMs largely differed from P sorption experiments regarding the sorption capacities and Langmuir coefficients, and it simulated an overly low soil P-buffering capacity. Conversely, the double-surface Langmuir isotherm approach adequately reproduced the observed patterns of soil inorganic P pools. The better representation of inorganic P cycling using the double-surface Langmuir approach also improved simulated foliar N and P concentrations as well as the patterns of gross primary production and vegetation carbon across the soil P gradient. The novel model generally reduces the estimates of P limitation compared with the conventional model, particularly at the low-P site, as the model constraint of slow inorganic P exchange on plant productivity is reduced.

Terrestrial ecosystem acts as important carbon dioxide (CO2) sinks and nitrous oxide (N2O) sources. Ecosystem green-house-gas fluxes could further lead to a climate feedback, which are highly correlated with the C-N coupling. However, magnitudes of such composited feedbacks as well as contributions by individual ecological processes remained certain uncertainties. Here, we applied a terrestrial biosphere model QUINCY with fully C-N-coupling to comprehensively examine responses of CO2 and N2O fluxes to future climate changes and quantify contributions by individual processes. Our results showed that the trade-offs in CO2 and N2O still led a negative feedback (-3386.9 Tg CO2 yr-1) averaged over 2050-2100 under the SSP 5-8.5 scenario relative to SSP 1-2.6 scenario, however, which varies from -1761.7 Tg CO2 yr-1 to -5012.1 CO2 yr-1 with a high or low climate sensitivity to CO2 increases. Further process analysis showed that the CO2 fertilization effects on ecosystem climate feedbacks were equivalent for high and low climate sensitivity, but the higher climate sensitivity led to less carbon sequestration on tropical plants as well as higher N2O emissions. The climate feedbacks attributed to individual soil processes, including decomposition, nitrification, denitrification and nitrogen biological fixation, were also comprehensively quantified. This finding suggests that reducing the uncertainties in climate sensitivity estimates could be of great significance to better predict future terrestrial ecosystem green-house-gas fluxes as well as corresponding climate feedbacks.

Understanding the interactions between atmosphere and vegetation in changing climatic conditions is important so that we can predict the carbon sequestration potential of ecosystems. Helpful tools here are the terrestrial biosphere models (TBMs), since they include detailed ecophysiological process descriptions, e.g. the manifold interactions between the carbon and nitrogen cycles. However, the modelling of the nitrogen cycle poses challenges and having observational constraints on nitrogen cycle is crucial. Current remote sensing products offer estimates of leaf chlorophyll (Cab), that is related to the nitrogen cycle. In this study we want to assess how useful Cab observations are at site scale to constrain a TBM. In this work we are studying a temperate mixed forest, Borden, located in Canada. We use a TBM QUantifying Interactions between terrestrial Nutrient Cycles, QUINCY, to model this site. From the site we have long-term (20 years) flux tower and LAI (from PAR measurements) observations together with leaf level observations of leaf chlorophyll (Cab), leaf nitrogen, and photochemical parameters of maximum carboxylation rate (Vcmax) and maximum potential electron transport rate (Jmax). The QUINCY model was predicting too late leaf senescence, which we tuned using the site level data. The amount of leaf nitrogen was originally quite successfully simulated by QUINCY, but the amount of simulated Cab was too low. Matching the simulated Cab values with the observations did not have a pronounced effect on the GPP. Additionally, the development of LAI and Cab were originally fully coupled in QUINCY, whereas the observations showed a delayed development of Cab compared to LAI. When we implemented this decoupling between LAI and Cab, an improvement of simulated GPP compared to the observations was found. Also then the simulated Vcmax and Jmax showed better correspondence to the observations. Assessment of the long-term behaviour of the model at the site showed that the model was able to capture the drought-induced drawdown of carbon fluxes taking place in 2007. The observations showed an increase in the component fluxes of carbon during the time period, but this was not replicated by the model. The start of season (SOS) and end of season (EOS) were estimated from both the simulated and observed GPP and LAI using a simple threshold method. The model was more successful in capturing the changes in the growing season metrics estimated by LAI than by GPP. The model was predicting too late onset of GPP in many years, but captured largely the interannual variation of SOS in observed GPP. This study paves the way for work using remotely sensed leaf chlorophyll in evaluation and improvement of the QUINCY model.

Ecosystem manipulative experiments are one of the most powerful tools to understand terrestrial ecosystem responses to global change because they measure real responses in real ecosystems. However, their scope is limited in space and time due to cost and labour intensity. This makes generalising results from such experiments difficult, which creates a conceptual gap between local scale process understanding and global scale future predictions. Recent efforts have seen results from such experiments used in combination with dynamic global vegetation models, most commonly to evaluate model predictions under global change drivers. However, there is much more potential in combining models and experiments. Here, we discuss the value and potential of a workflow for using ecosystem experiments together with process-based models to enhance the potential of both. We suggest that models can be used prior to the start of an experiment to generate hypotheses, identify data needs and in general guide experimental design. Models, when adequately constrained with observations, can also predict variables which are difficult to measure frequently or at all, and together with the data can provide a more complete picture of ecosystem states. Finally, models can be used to help generalise the experimental results in space and time, by providing a framework in which process understanding derived from site-level experiments can be incorporated. We also discuss the potential for using manipulative experiments together with models in formalised model-data integration frameworks for parameter estimation and model selection, a path made possible by the increasing number of ecosystem experiments and diverse observation streams. The ideas presented here can provide a roadmap to future experiment - model studies.

The effect of nutrient availability on plant growth and the terrestrial carbon sink under climate change and elevated CO2 remains one of the main uncertainties of the terrestrial carbon cycle. This is partially due to the difficulty of assessing nutrient limitation at large scales over long periods of time. Consistent declines in leaf nitrogen (N) content and leaf δ15 N have been used to suggest that nitrogen limitation has increased in recent decades, most likely due to the concurrent increase in atmospheric CO2 . However, such data sets are often not straightforward to interpret due to the complex factors that contribute to the spatial and temporal variation in leaf N and isotope concentration. We use the land surface model (LSM) QUINCY, which has the unique capacity to represent N isotopic processes, in conjunction with two large data sets of foliar N and N isotope content. We run the model with different scenarios to test whether foliar δ15 N isotopic data can be used to infer large-scale N limitation and if the observed trends are caused by increasing atmospheric CO2 , changes in climate or changes in sources and magnitude of anthropogenic N deposition. We show that while the model can capture the observed change in leaf N content and predict widespread increases in N limitation, it does not capture the pronounced, but very spatially heterogeneous, decrease in foliar δ15 N observed in the data across the globe. The addition of an observation-based temporal trend in isotopic composition of N deposition leads to a more pronounced decrease in simulated leaf δ15 N. Our results show that leaf δ15 N observations cannot, on their own, be used to assess global-scale N limitation and that using such a data set in conjunction with an LSM can reveal the drivers behind the observed patterns.

Biogeochemical cycling in permafrost-affected ecosystems remains associated with large uncertainties, which could impact the Earth's greenhouse gas budget and future climate policies. In particular, increased nutrient availability following permafrost thaw could perturb the greenhouse gas exchange in these systems, an effect largely unexplored until now. Here, we enhance the terrestrial ecosystem model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), which simulates fully coupled carbon (C), nitrogen (N) and phosphorus (P) cycles in vegetation and soil, with processes relevant in high latitudes (e.g., soil freezing and snow dynamics). In combination with site-level and satellite-based observations, we use the model to investigate impacts of increased nutrient availability from permafrost thawing in comparison to other climate-induced effects and CO

Phosphorus (P) availability affects the response of terrestrial ecosystems to environmental and climate change (e.g. elevated CO2), yet the magnitude of this effect remains uncertain. This uncertainty arises mainly from a lack of quantitative understanding of the soil biological and geochemical P cycling processes, particularly the P exchange with soil mineral surfaces, which is often described by a Langmuir sorption isotherm. We first conducted a literature review on P sorption experiments and terrestrial biosphere models (TBMs) using Langmuir isotherm. We then developed a new algorithm to describe the inorganic P exchange between soil solution and soil matrix based on the double-surface Langmuir isotherm and extracted empirical equations to calculate the sorption capacity and Langmuir coefficient. We finally tested the conventional and new models of P sorption at five beech forest sites in Germany along a soil P stock gradient using the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. We found that the conventional (single-surface) Langmuir isotherm approach in most TBMs largely differed from P sorption experiments regarding the sorption capacities and Langmuir coefficients, and simulated a too low soil P buffering capacity. Conversely, the double-surface Langmuir isotherm approach adequately reproduced the observed patterns of soil inorganic P pools. The better representation of inorganic P cycling using the double Langmuir approach also improved simulated foliar N and P concentrations, and the patterns of gross primary production and vegetation carbon across the soil P gradient. The novel model generally reduces the estimates of P limitation compared to the conventional model, particularly at the low-P site, as the model constraint of slow inorganic P exchange on plant productivity is reduced.

There is a pressing need to better understand ecosystem resilience to droughts and heatwaves. Eco-evolutionary optimization approaches have been proposed as means to build this understanding in land surface models and improve their predictive capability, but competing approaches are yet to be tested together. Here, we coupled approaches that optimize canopy gas exchange and leaf nitrogen investment, respectively, extending both approaches to account for hydraulic impairment. We assessed model predictions using observations from a native Eucalyptus woodland that experienced repeated droughts and heatwaves between 2013 and 2020, whilst exposed to an elevated [CO2] treatment. Our combined approaches improved predictions of transpiration and enhanced the simulated magnitude of the CO2 fertilization effect on gross primary productivity. The competing approaches also worked consistently along axes of change in soil moisture, leaf area, and [CO2]. Despite predictions of a significant percentage loss of hydraulic conductivity due to embolism (PLC) in 2013, 2014, 2016, and 2017 (99th percentile PLC > 45%), simulated hydraulic legacy effects were small and short-lived (2 months). Our analysis suggests that leaf shedding and/or suppressed foliage growth formed a strategy to mitigate drought risk. Accounting for foliage responses to water availability has the potential to improve model predictions of ecosystem resilience.

Phosphorus (P) is an essential macro-nutrient required for plant metabolism and growth. Low P availability could potentially limit plant responses to elevated carbon dioxide (eCO2), but consensus has yet to be reached on the extent of this limitation. Here, based on data from experiments that manipulated both CO2 and P for young individuals of woody and non-woody species, we present a meta-analysis of P limitation impacts on plant growth, physiological, and morphological response to eCO2. We show that low P availability attenuated plant photosynthetic response to eCO2 by approximately one-quarter, leading to a reduced, but still positive photosynthetic response to eCO2 compared to those under high P availability. Furthermore, low P limited plant aboveground, belowground, and total biomass responses to eCO2, by 14.7%, 14.3%, and 12.4%, respectively, equivalent to an approximate halving of the eCO2 responses observed under high P availability. In comparison, low P availability did not significantly alter the eCO2-induced changes in plant tissue nutrient concentration, suggesting tissue nutrient flexibility is an important mechanism allowing biomass response to eCO2 under low P availability. Low P significantly reduced the eCO2-induced increase in leaf area by 14.3%, mirroring the aboveground biomass response, but low P did not affect the eCO2-induced increase in root length. Woody plants exhibited stronger attenuation effect of low P on aboveground biomass response to eCO2 than non-woody plants, while plants with different mycorrhizal associations showed similar responses to low P and eCO2 interaction. This meta-analysis highlights crucial data gaps in capturing plant responses to eCO2 and low P availability. Field-based experiments with longer-term exposure of both CO2 and P manipulations are critically needed to provide ecosystem-scale understanding. Taken together, our results provide a quantitative baseline to constrain model-based hypotheses of plant responses to eCO2 under P limitation, thereby improving projections of future global change impacts.

Nutrient cycles are tightly linked to the carbon cycle in tropical forests, controlling its responses to environmental change such as elevated CO2 concentrations (eCO2). In tropical wet forests of the Amazon, plants tend to grow slower in low fertility soils, while their relative investments in nutrient acquisition are likely higher due to costly mechanisms of nutrient mobilization. In low fertility soils where weatherable minerals have been depleted, decomposition and mineralization of soil organic matter and plant litter by free-living microorganisms may represent the dominant nutrient source for plants. The availability of soil nutrients for plants is a key constraint on tropical forest growth under future climate change. However, soil microbial communities not only drive the mineralization, but require nutrients for growth and biomass production themselves, and therefore from a plant’s perspective, soil microbial communities may act as a source or sink of nutrients under eCO2.Here, we employ the process-based terrestrial biosphere model QUINCY (Thum et al., 2019), coupled to the microbial-explicit soil model JSM (Yu et al., 2020) to model shifts in coupled nutrient and carbon cycling rates at field sites with differing soil fertility in the Amazon forest sites, and to confront the modeled ecosystems with elevated CO2. QUINCY-JSM reflects our current understanding of governing carbon and nutrient cycling feedbacks, allowing for dynamic plant carbon investment in growth and nutrient acquisition, and microbial-explicit growth, turnover, and nutrient cycling. We compiled a unique dataset of forest growth, soil and litter chemistry, as well as microbial growth and stoichiometry from a set of Amazon forest plots that cover a large gradient in soil fertility. Microbial stoichiometry and soil texture data are used to calibrate QUINCY-JSM, and data on forest aboveground growth, microbial growth, litter chemistry, and soil carbon and nutrients are used for model evaluation. We test the hypothesis that the contribution of microbial-driven nutrient mineralization to the nutrient supply of plants increases with lowering soil fertility and explore the soil microbial-induced nutrient feedback to eCO2-induced carbon sequestration in wet lowland Amazon forest sites. We examine the consequences of model assumptions on the conditions in space and time under which soil microorganisms alleviate or enforce the plants’ nutrient limitation under eCO2. Directly testable hypotheses for old-growth wet forests’ response to elevated CO2 in ecosystem-scale experiments like AmazonFACE are formulated.Thum, T., Caldararu, S., Engel, J., Kern, M., Pallandt, M., Schnur, R., et al. (2019). A new model of the coupled carbon, nitrogen, and phosphorus cycles in the terrestrial biosphere (QUINCY v1.0; revision 1996). Geoscientific Model Development, 12(11), 4781–4802. https://doi.org/10.5194/gmd-12-4781-2019Yu, L., Ahrens, B., Wutzler, T., Schrumpf, M., & Zaehle, S. (2020). Jena Soil Model (JSM v1.0; Revision 1934): A microbial soil organic carbon model integrated with nitrogen and phosphorus processes. Geoscientific Model Development, 13(2), 783–803. https://doi.org/10.5194/gmd-13-783-2020

Thawing and degradation of permafrost in high latitudes could have an important effect on the Earth’s greenhouse budget. Implications of increased nutrient availability resulting from thawing of nutrient-rich permafrost, however, remain poorly assessed, despite nutrients having being identified as a strong present-day constraint for plant growth and microbial activity in the high latitudes. In our pan-arctic scale study, we extend the terrestrial ecosystem model QUINCY, which already couples C-N-P cycles in soil and vegetation, for a better representation of high-latitude processes. With this model version, we perform historical simulations at the site-level over 1960-2019. Averaged over high-latitude grassland sites, our simulations show an average increase in the soil active layer depth of 0.1m and an increased gradient of biologically-available P and N at the permafrost front. In spite of this, only 11 % of the simulated increase over the GPP (+34%) is a result of increased nutrient supply from permafrost organic matter degradation. This owes to spatial and temporal decoupling of the simulated vegetation growth peak (mid-to-late-July), the time period where plant nutrient demand is the highest, and the maximum of the seasonal thaw depth (mid-to-late August), the time period in which nutrients in the deep active layer would potentially be available for uptake. As a result, increased nitrogen at the permafrost front and alternating aerobic-anaerobic conditions contribute to enhancing nitrification and denitrification in the model, causing a weak source of N2O to the atmosphere of 0.7 kg N ha-1 yr-1, which undergoes a considerable upward trend of up to 0.1 kg N ha-1 decade-1, locally,over the simulation time frame. Considering the vastness of the permafrost domain, and that N2O emissions from these regions have been largely neglected in the past, these results imply that high latitudes could be a considerable and growing contributor to the global atmospheric N2O budget.

Predicting ecosystem resilience to droughts and heatwaves requires a predictive capacity that is currently lacking in land-surface models (LSMs). Eco-evolutionary optimisation approaches have the potential to increase predictability, but competing approaches are yet to be probed together in LSMs. Here, we coupled schemes that optimise canopy gas-exchange vs. leaf nitrogen investment, and both approaches were extended to account for hydraulic legacies from water-stress. We assessed model predictions using observations from a South-Eastern Australian woodland exposed to repeated drought between 2013 and 2020, under both ambient and elevated [CO2]. Our simulations were in good agreement with observations of transpiration (r2∼0.7), leaf water potential (±0.1 MPa), and leaf photosynthetic capacities (±5% of the observations). Despite predictions of significant percentage loss of conductivity (PLC) due to water stress in 2013, 2014, 2016, and 2017 (p99 > 45%), hydraulic legacy effects were small and recovered rapidly. Combining the optimisation schemes and hydraulic legacies led to improved model predictions and enhanced the simulated magnitude fertilisation effect on GPP at elevated [CO2], albeit that the impact on the canopy fluxes was small overall. Our simulations suggested that leaf shedding and/or suppressed foliage growth formed an active strategy to mitigate drought risk, with leaves being grown during wet years to replenish carbon stores, whereas LAI dropped in anticipation of severe water stress to prevent high PLC. Accounting for leaf acclimation in response to drought therefore has the potential to improve predictions of ecosystem resilience to drought in water-limited regions.

Leaf nutrient contents, in particular nitrogen (N) and phosphorus (P), are key plant traits, linking to processes such as photosynthesis and respiration. Traditionally, plant traits are considered to be constant in time, but there is ample observational evidence that nutrient content varies with changes in environmental conditions. Specifically, increased atmospheric CO2 drives increased plant growth and potentially increases in plant nutrient limitation, while anthropogenic N deposition further drives an imbalance in the N and P available to plants. Therefore, being able to dynamically, and accurately, represent leaf N and P content in land surface models (LSMs) is critical to predicting future ecosystem response to global change.Most LSMs that include dynamic leaf N and P do so through a set of empirical functions that balance out demand and supply of nutrients. However, such representations do not take into account the differing physiological roles of the two nutrients, with N being directly linked to photosynthetic compounds, while P has a much stronger control on new biomass growth and respiration. Using the QUINCY land surface model we represent physiologically-realistic dynamic leaf N and P content, using optimality theory, which assumes that plants alter their structure and function in order to maximize growth. We test the model using data from ICP Forests, a spatially extensive European network of sites with repeated, standardized measurements of leaf N and P content covering the period 1990 - present. We show that the new model representation performs better than the standard empirical functions, both in terms of spatial distribution of leaf nutrient content and its change through time, being able to reproduce the observed shift towards P limitation caused by increased N deposition. Most importantly, the model does not rely on the law of the minimum principle, being able to represent true co-limitation and allow leaf N and P to vary with different physiological and environmental pressures, thus creating more robust and realistic predictions.

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8., Three key axes of variation of ecosystem functional changes and their underlying causes are identified from a dataset of surface gas exchange measurements across major terrestrial biomes and climate zones.

The effect of nutrient availability on plant growth and the terrestrial carbon sink under climate change and elevated CO2 remains one of the main uncertainties of the terrestrial carbon cycle. This is partially due to the difficulty of assessing nutrient limitation at large scales over long periods of time. Consistent declines in leaf nitrogen (N) content and leaf δ15N have been used to suggest that nitrogen limitation has increased in recent decades, most likely due to the concurrent increase in atmospheric CO2. However, such datasets are often not straightforward to interpret due to the complex factors that contribute to the spatial and temporal variation in leaf N and isotope concentration. We use the land surface model QUINCY, which has the unique capacity to represent N isotopic processes, in conjunction with two large datasets of foliar N and N isotope content. We run the model with different scenarios to test whether foliar δ15N isotopic data can be used to infer large scale nitrogen limitation and if the observed trends are caused by increasing atmospheric CO2, changes in climate or changes in sources of anthropogenic N deposition. We show that while the model can capture the observed change in leaf N content and predicts widespread increases in N limitation, it does not capture the pronounced, but very spatially heterogeneous, decrease in foliar δ15N observed in the data across the globe. The addition of an observed temporal trend in isotopic composition of N deposition leads to a more pronounced decrease in simulated leaf δ15N. Our results show that leaf δ15N observations should not, on their own, be used to assess global scale N limitation and that using such a dataset in conjunction with a land surface model can reveal the drivers behind the observed patterns.

Phosphorus (P) is a macro nutrient affecting the terrestrial ecosystem productivity and future carbon (C) balance, and is known as the main constraint for C sequestration in tropical ecosystems, such as Amazon rainforests and Eucalyptus forests. However, in temperate and boreal forests, nitrogen (N) is usually considered as the main limiting nutrient for plant growth and the role of P cycling is less highlighted. Through a comprehensive investigation of soil and biological properties of five European beech forest sites along a wide range of natural soil P stocks, Lang et al. (2017) have concluded that P availability can shape plant-microorganism-soil interactions in forest ecosystem and the P cycling strategy switches from an “acquiring strategy” to a “recycling strategy” as the soil P availability decreases. The switch from a P-rich “acquiring strategy” to a P-poor “recycling strategy” is significantly concurrent with decreasing forest floor turnover rates (from 1/5 to 1/40 per year), increasing organic C to P ratios (from 110 to 984; A horizons), and increasing proportions of fine-root biomass in the forest floor (from 10% to 80%), as well as some less significant ecosystem properties, such as the total soil organic C and N, resorption of leaf P before senescence, and P concentrations in leaf litter and fine roots. However, none of the vegetative traits (foliar N and P contents, tree height, basal area, and tree volume) changed systematically along the soil P gradient, indicating that the shift in soil P cycling strategies allows for satisfying plant P demand.This observational evidence imposes great challenges on the modeling of forest ecosystems, especially on the modeling of soil processes and plant-soil interactions. In conventional models, several of these features cannot be reproduced, due to 1) fixed first-order kinetics of decomposition, 2) lack of mechanisms for soil C storage/stabilization, 3) lack of explicit microbial dynamics and processes, 4) fixed stoichiometry in plant and soil, 5) lack of dynamic plant C allocation to roots and root exudation, 6) ignorance of C cost for nutrient mobilization.In this presentation, we would like to propose modeling solutions to these challenges based on a novel modeling framework of QUINCY (Thum et al. 2019) and JSM (Yu et al. 2020). The new model allows separation of the sink (photosynthesis) and source (nutrients and water availabilities) in plant growth and applies a dynamic C allocation to maximize assimilation of limiting resources. It is vertical- and microbial-explicit in the soil processes, and mechanistically describes the effects of microbial dynamics on soil C decomposition and stabilization as well as nutrient mobilization. The preliminary results have shown increasing C investments from plant-microbe to soil P mobilization with decreasing soil P availability. It allows us to reproduce the observed patterns in litter turnover, root allocation, soil C storage, and soil stoichiometry, indicating the important role of C-nutrient interactions in the P cycling modeling. We hope with the advances of modeling P cycling strategies in temperate forest ecosystems, we could also better model P cycling in the tropics and better project future C sequestration globally.

Droughts have been implicated as the driver behind recent vegetation die-off across a variety of hydroclimates and are projected to drive greater mortality under future climate change. Predicting ecosystem resilience to future drought requires a predictive capacity, which is currently lacking in state-of-the-art land surface models (LSMs) that rely on simplified empirical relationships to represent the impacts of water stress on vegetation. Novel approaches that optimise stomatal conductance with respect to plant photosynthetic and hydraulic functions have been shown to reduce the biases of LSM gas exchange predictions during drought. These approaches also offer a pathway to further develop mechanistic optimality theory, e.g. pertaining to leaf drought deciduousness. But on what timescale(s) does vegetation function adjust to maximise resource investment? We explore the following timescales of optimality within a simple LSM: (i) instantaneous (regulating canopy gas exchange); (ii) monthly (regulating the investment of nitrogen in photosynthetic capacity); and (iii) seasonal to annual (water stress legacies on plant hydraulics). We use observations from a temperate woodland in South-Eastern Australia to test which optimisation timescales and processes are best supported and whether competing timescales can operate together, both under well-watered conditions and during a severe multi-year drought, and from the leaf-scale to the ecosystem-scale. The insights gained help us characterize how adjoined allocation processes, like leaf biomass adjustment, relate to leaf carbon uptake and plant water status through time (e.g. leaves can be shed to mitigate drought stress or built from structural storage pools when water is not limiting), therefore conferring additional resilience.

Increasing atmospheric CO2 concentrations can be a driver for higher ecosystem productivity across the globe but nutrient availability may limit subsequent biomass growth. Concurrently, increased anthropogenic nitrogen (N) deposition introduces a relatively large amount of N into the system, thus potentially alleviating N limitation. However, this new N input could push ecosystems into being limited by other resources, most importantly phosphorus (P) in mid- and high-latitude systems, leading to what has been termed an NP imbalance. While the ecological theory behind the processes described above has been discussed on many occasions, it is yet unclear what the actual spatial and temporal patterns of such an imbalance are, as well as the ecpological processes and drivers behind such observed patterns.Here, we use leaf N and P data from a large European monitoring network, ICP forests, in conjunction with a land surface model, QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), to explore the patterns and drivers behind nutrient limitation at European forest sites. The overall trend in observed leaf N and P content as well as N:P ratio show an increasing nutrient limitation from 1990 to 2015, as well as a shift towards P limitation. However, the observed spatial patterns of change in leaf nutrient content vary strongly with soil nutrient availability, N deposition and leaf habit. The effect of leaf habit suggests that leaf growth strategies play an important role in dealing with nutrient availability and controlling observed ecosystem responses. We use the QUINCY model to explore the drivers behind the observed leaf nutrient trends. We perform simulations with fixed levels of atmospheric CO2 as well as in the absence of anthropogenic nitrogen deposition. We show that the decrease in leaf N and P content is attributable to increased atmospheric CO2, while the changes in N:P stoichiometry are reproducible with increased N deposition. Additionally, the model can only predict observed trends when representing physiologically-realistic responses of leaf stoichiometry to nutrient availability. The use of a process-based model allows us to attribute drivers to the observed changes in leaf nutrient content. This research helps the development of data-constrained, process-based models which can potentially be used to predict changes in ecosystem nutrient limitation, and implicitly growth and carbon storage, under future scenarios

Terrestrial vegetation growth is hypothesised to increase under elevated atmospheric CO2, a process known as the CO2 fertilisation effect. However, the magnitude of this effect and its long-term sustainability remains uncertain. One of the main limitations to the CO2 fertilisation effect is nutrient limitation to plant growth, in particular nitrogen (N) in temperate and boreal ecosystems. Recent studies have suggested that decreases in observed foliar N content (N%) and δ15N indicate widespread nitrogen limitation with increasing CO2 concentrations. However, the factors driving these two variables, and especially the foliar δ15N values, are complex and can be caused by a number of processes. On one hand, if the observed trends reflect nutrient limitation, this limitation can be caused by either CO2 or warming driven growth. On the other hand, it is possible that nutrient limitation does not occur to its full extent due to plant plastic responses to alleviate nutrient limitation, causing a decrease in N%, but changes in the anthropogenic N deposition 15N signal cause the observed δ15N trend. In reality, it is likely that all these factors contribute to the observed trends. To understand ecosystem dynamics it is important to disentangle the processes behind these signals which is very difficult based on observational datasets only.We use a novel land surface model to explore the causes behind the observed trends in foliar N% and δ15N. The QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) model has the unique capacity to track ecologically relevant isotopic composition, including 15N in plant and soil pools. The model also includes a realistic representation of plant plastic acclimation processes, specifically a representation of nitrogen allocation to and inside the canopy in response to nitrogen availability, so implicitly to changes in CO2 concentrations. We test the different hypotheses above behind the observed changes in N% and δ15N separately and quantify the contribution of each of the factors towards the observed trend. We then test the different hypotheses against existing observations of N% and δ15N from the ICP Forests database and other published datasets such as the global dataset of Craine et al. 2018.Our study showcases the use of an isotope-enabled land surface model in conjunction with long-term observations to strengthen our understanding of the ecosystem processes behind the observed trends.

Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In the current paper we present a generic process based crop model which we parametrise using a Bayesian model fitting algorithm to three different sources of data – space based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters can largely capture the observed behaviour but the data constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improves on the prior model fit, the satellite based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge10 gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection steps for improvement in our predictions of crop yields and crop responses to environmental changes.

Phosphorus (P) is an essential macro-nutrient required for plant metabolism and growth. Low P availability could potentially limit plant responses to elevated carbon dioxide (eCO

The dynamics of terrestrial ecosystems are shaped by the coupled cycles of carbon, nitrogen and phosphorus, and strongly depend on the availability of water and energy. These interactions shape future terrestrial biosphere responses to global change. Many process-based models of the terrestrial biosphere have been gradually extended from considering carbon-water interactions to also including nitrogen, and later, phosphorus dynamics. This evolutionary model development has hindered full integration of these biogeochemical cycles and the feedbacks amongst them. Here we present a new terrestrial ecosystem model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), which is formulated around a consistent representation of element cycling in terrestrial ecosystems. This new model includes i) a representation of plant growth which separates source (e.g. photosynthesis) and sink (growth rate of individual tissues, constrained by nutrients, temperature, and water availability) processes; ii) the acclimation of many ecophysiological processes to meteorological conditions and/or nutrient availabilities; iii) an explicit representation of vertical soil processes to separate litter and soil organic matter dynamics; iv) a range of new diagnostics (leaf chlorophyll content; 13C, 14C, and 15N isotope tracers) to allow for a more in-depth model evaluation. We present the model structure and provide an assessment of its performance against a range of observations from global-scale ecosystem monitoring networks. We demonstrate that the framework is capable of consistently simulating ecosystem dynamics across a large gradient in climate and soil conditions, as well as across different plant functional types. To aid this understanding we provide an assessment of the model's sensitivity to its parameterisation and the associated uncertainty.

SummaryVegetation nutrient limitation is essential for understanding ecosystem responses to global change. In particular, leaf nitrogen (N) is known to be plastic under changed nutrient limitation. However, models can often not capture these observed changes, leading to erroneous predictions of whole-ecosystem stocks and fluxes.We hypothesise that an optimality approach can improve representation of leaf N content compared to existing empirical approaches. Unlike previous optimality-based approaches, which adjust foliar N concentrations based on canopy carbon export, we use a maximisation criteria based on whole-plant growth and allow for a lagged response of foliar N to this maximisation criterion to account for the limited plasticity of this plant trait. We test these model variants at a range of Free-Air CO2 Enrichment (FACE) and N fertilisation experimental sites.We show a model solely based on canopy carbon export fails to reproduce observed patterns and predicts decreasing leaf N content with increased N availability. However, an optimal model which maximises total plant growth can correctly reproduce the observed patterns.The optimality model we present here is a whole-plant approach which reproduces biologically realistic changes in leaf N and can thereby improve ecosystem-level predictions under transient conditions.

Vegetation nutrient limitation is essential for understanding ecosystem responses to global change. In particular, leaf nitrogen (N) is known to be plastic under changed nutrient limitation. However, models can often not capture these observed changes, leading to erroneous predictions of whole-ecosystem stocks and fluxes. We hypothesise that an optimality approach can improve representation of leaf N content compared to existing empirical approaches. Unlike previous optimality-based approaches, which adjust foliar N concentrations based on canopy carbon export, we use a maximisation criterion based on whole-plant growth, and allow for a lagged response of foliar N to this maximisation criterion to account for the limited plasticity of this plant trait. We test these model variants at a range of Free-Air CO

Contents Summary 1223 I. Introduction 1223 II. Photosynthesis and respiration 1224 III. Biomass growth 1224 IV. Carbon allocation 1225 V. Plant internal P redistribution 1226 VI. Plant P uptake 1227 VII. Conclusion 1227 Acknowledgements 1228 References 1228 SUMMARY: Our ability to understand the effect of nutrient limitation on ecosystem productivity is key to the prediction of future terrestrial carbon storage. Significant progress has been made to include phosphorus (P) cycle processes in land surface models (LSMs), but these efforts are focused on the soil component of the P cycle. Incorporating the soil component is important to estimate plant-available P, but does not necessarily address the vegetation response to P limitation or plant-soil interactions. A more detailed representation of plant P processes is needed to link nutrient availability and ecosystem productivity. We review physiological and biochemical evidence for vegetation responses to P availability, and recommend ways to move towards a more physiological representation of vegetation P processes in LSMs.

Phenology is essential to our understanding of biogeochemical cycles and the climate system. We develop a global mechanistic model of leaf phenology based on the hypothesis that phenology is a strategy for optimal carbon gain at the canopy level so that trees adjust leaf gains and losses in response to environmental factors such as light, temperature and soil moisture, to achieve maximum carbon assimilation. We fit this model to five years of satellite observations of leaf area index (LAI) using a Bayesian fitting algorithm. We show that our model is able to reproduce phenological patterns for all vegetation types and use it to explore variations in growing season length and the climate factors that limit leaf growth for different biomes. Phenology in wet tropical areas is limited by leaf age physiological constraints while at higher latitude leaf seasonality is limited by low temperature and light availability. Leaf growth in grassland regions is limited by water availability but often in combination with other factors. This model will advance the current understanding of phenology for ecosystem carbon models and our ability to predict future phenological behaviour.

Seasonal and year-to-year variations in leaf cover imprint significant spatial and temporal variability on biogeochemical cycles, and affect land-surface properties related to climate. We develop a demographic model of leaf phenology based on the hypothesis that trees seek an optimal Leaf Area Index (LAI) as a function of available light and soil water, and fitted it to spaceborne observations of LAI over the Amazon Basin, 2001–2005. We find the model reproduces the spatial and temporal LAI distribution whilst also predicting geographic variation in leaf age from the basin center (2.1 ± 0.2 yr), through to the lowest values over the deciduous Eastern Amazon (6 ± 2 months). The model explains the observed increase in LAI during the dry season as a net addition of leaves in response to increased solar radiation. We anticipate our work to be a starting point from which to develop better descriptions of leaf phenology to incorporate into more sophisticated earth system models.

Leaf seasonality impacts a variety of important biological, chemical, and physical Earth system processes, which makes it essential to represent leaf phenology in ecosystem and climate models. However, we are still lacking a general, robust parametrisation of phenology at global scales. In this study, we use a simple process-based model, which describes phenology as a strategy for carbon optimality, to test the effects of the common simplification in global modelling studies that plant species within the same plant functional type (PFT) have the same parameter values, implying they are assumed to have the same species traits. In a previous study this model was shown to predict spatial and temporal dynamics of leaf area index (LAI) well across the entire global land surface provided local grid cell parameters were used, and is able to explain 96 % of the spatial variation in average LAI and 87 % of the variation in amplitude. In contrast, we find here that a PFT level parametrisation is unable to capture the spatial variability in seasonal cycles, explaining on average only 28 % of the spatial variation in mean leaf area index and 12 % of the variation in seasonal amplitude. However, we also show that allowing only two parameters, light compensation point and leaf age, to be spatially variable dramatically improves the model predictions, increasing the model's capability of explaining spatial variations in leaf seasonality to 70 and 57 % of the variation in LAI average and amplitude, respectively. This highlights the importance of identifying the spatial scale of variation of plant traits and the necessity to critically analyse the use of the plant functional type assumption in Earth system models.

The present paper presents a hardware simulator of an integrated array of microminiaturized SnO 2 sensors and their preconditioning circuit for measurement of CO, NO x and VOC (Volatile Organic Compounds) pollutants emitted in urban traffic. The SnO 2 sensor array is simulated by three discrete SnO 2 sensors together with their conditioning circuitry and the power supply coupled to a microcontroller. SnO 2 sensors are characterized by short response times and high fiability, but low discrimination. The design main goal is to surpass this problem through technological design of the sensors, electrical circuit optimization and special software for the measured data. The hardware simulator includes three low power sensors, obtained on SnO 2 deposited on ceramic, analogue with the sensors which will be integrated in the array. Experimental calibration curves were obtained, using a special calibration chamber for each of above gases. The calibration and measured data are included in a special designed software, using Visual Basic 6.0 and a subroutine of Fortran Power Station Library. The hardware simulator presented in this paper was used to measure the concentrations of the pollutants emitted from the Bucharest urban traffic, and are in good agreement with those reported by the Romanian National Agency for Environmental Protection.

Elevated carbon dioxide (CO2) can increase plant growth, but the magnitude of this CO2 fertilization effect is modified by soil nutrient availability. Predicting how nutrient availability affects plant responses to elevated CO2 is a key consideration for ecosystem models, and many modeling groups have moved to, or are moving towards, incorporating nutrient limitation in their models. The choice of assumptions to represent nutrient cycling processes has a major impact on model predictions, but it can be difficult to attribute outcomes to specific assumptions in complex ecosystem simulation models. Here we revisit the quasi-equilibrium analytical framework introduced by Comins and McMurtrie (1993) and explore the consequences of specific model assumptions for ecosystem net primary productivity (NPP). We review the literature applying this framework to plant–soil models and then analyze the effect of several new assumptions on predicted plant responses to elevated CO2. Examination of alternative assumptions for plant nitrogen uptake showed that a linear function of the mineral nitrogen pool or a linear function of the mineral nitrogen pool with an additional saturating function of root biomass yield similar CO2 responses at longer timescales (>5 years), suggesting that the added complexity may not be needed when these are the timescales of interest. In contrast, a saturating function of the mineral nitrogen pool with linear dependency on root biomass yields no soil nutrient feedback on the very-long-term (>500 years), near-equilibrium timescale, meaning that one should expect the model to predict a full CO2 fertilization effect on production. Secondly, we show that incorporating a priming effect on slow soil organic matter decomposition attenuates the nutrient feedback effect on production, leading to a strong medium-term (5–50 years) CO2 response. Models incorporating this priming effect should thus predict a strong and persistent CO2 fertilization effect over time. Thirdly, we demonstrate that using a “potential NPP” approach to represent nutrient limitation of growth yields a relatively small CO2 fertilization effect across all timescales. Overall, our results highlight the fact that the quasi-equilibrium analytical framework is effective for evaluating both the consequences and mechanisms through which different model assumptions affect predictions. To help constrain predictions of the future terrestrial carbon sink, we recommend the use of this framework to analyze likely outcomes of new model assumptions before introducing them to complex model structures.