Biosphere Model Research Articles

Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model

AbstractPredicting terrestrial carbon, C, budgets and carbon‐climate feedbacks strongly relies on our ability to accurately model interactions between vegetation, C and water cycles, and the atmosphere. However, C fluxes simulated by global, process‐based terrestrial biosphere models (TBMs) remain subject to large uncertainties, partly due to unknown or poorly calibrated parameters. This is because TBMs have not routinely been confronted against C cycle related datasets within a statistical data assimilation (DA) system. In this review, we present 15 years' development of a C cycle DA system for optimizing C cycle parameters of the ORCHIDEE TBM. We analyze the impact of assimilating multiple different C cycle related datasets on regional to global‐scale gross and net CO2 fluxes. We find that assimilating atmospheric CO2 data is crucial for improving (increasing) ORCHIDEE predictions of the terrestrial land C sink. The improvement is predominantly due to the global‐scale constraint these data provide for optimizing initial soil C stocks, which are likely in error due to inaccurate assumptions about steady state spin‐up and incomplete knowledge of land use change histories. When comparing the data‐constrained ORCHIDEE land C sink estimates to the CAMS atmospheric inversion, we show that while the two approaches agree on the global C sink magnitude, they continue to differ in how the global C sink is partitioned between the northern hemisphere and tropics. We also discuss technical challenges faced in our C cycle DA studies, in particular the difficulty in characterizing the error covariance matrix due to unknown observation biases and/or model‐data inconsistencies. We offer our perspectives on how to tackle these challenges that we hope can serve as a roadmap for other TBM groups wishing to develop C cycle DA systems.

Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)

Abstract. Terrestrial biosphere models (TBMs) are invaluable tools for studying plant–atmosphere interactions at multiple spatial and temporal scales, as well as how global change impacts ecosystems. Yet, TBM projections suffer from large uncertainties that limit their usefulness. Forest structure drives a significant part of TBM uncertainty as it regulates key processes such as the transfer of carbon, energy, and water between the land and the atmosphere, but it remains challenging to observe and reliably represent. The poor representation of forest structure in TBMs might actually result in simulations that reproduce observed land fluxes but fail to capture carbon pools, forest composition, and demography. Recent advances in terrestrial laser scanning (TLS) offer new opportunities to capture the three-dimensional structure of the ecosystem and to transfer this information to TBMs in order to increase their accuracy. In this study, we quantified the impacts of prescribing initial conditions (tree size distribution), constraining key model parameters with observations, as well as imposing structural observations of individual trees (namely tree height, leaf area, woody biomass, and crown area) derived from TLS on the state-of-the-art Ecosystem Demography model (ED2.2) of a temperate forest site (Wytham Woods, UK). We assessed the relative contributions of initial conditions, model structure, and parameters to the overall output uncertainty by running ensemble simulations with multiple model configurations. We show that forest demography and ecosystem functions as modelled by ED2.2 are sensitive to the imposed initial state, the model parameters, and the choice of key model processes. In particular, we show that: Parameter uncertainty drove the overall model uncertainty, with a mean contribution of 63 % to the overall variance of simulated gross primary production. Model uncertainty in the gross primary production was reduced fourfold when both TLS and trait data were integrated into the model configuration. Land fluxes and ecosystem composition could be simultaneously and accurately simulated with physically realistic parameters when appropriate constraints were applied to critical parameters and processes. We conclude that integrating TLS data can inform TBMs of the most adequate model structure, constrain critical parameters, and prescribe representative initial conditions. Our study also confirms the need for simultaneous observations of plant traits, structure, and state variables if we seek to improve the robustness of TBMs and reduce their overall uncertainties.

Low temperature and short daylength interact to affect the leaf senescence of two temperate tree species.

Temperature and photoperiod are two major environmental cues shaping the leaf senescence of temperate tree species. However, how the control of leaf senescence is split between photoperiod and temperature is unknown for many ecologically important species. Here, we conducted a growth chamber experiment to test the effects of temperature (6, 9, 18 and 21°C) and photoperiod (8 and 16h daylength) on leaf senescence of two temperate tree species (Quercus mongolica Fisch. and Larix principis-rupprechtii Mayr.) distributed in montane forest of China. The results showed that low temperature (LT) alone could induce leaf senescence of both species under long daylength (LD) conditions, but the leaf senescence of L. principis-rupprechtii was more sensitive to the decrease in temperature than that of Q. mongolica under the LD condition. Short daylength (SD) alone could only induce the leaf senescence of L. principis-rupprechtii, suggesting that the photoperiod sensitivity varies between species. SD could accelerate the LT-induced senescence, but the effect of SD reduced with the decrease in temperature. Based on these findings, we developed a new autumn phenology model by incorporating interspecific differences in the photoperiod sensitivity of leaf senescence. Compared with the three existing process-based autumn phenology models, the new model was more robust in simulating the experimental data. When employing these models to available long-term phenological data, our new model also performed best in reproducing the observed leaf senescence date of two closely related species (Quercus robur L. and Larix decidua Mill.). These results enhance our understanding of how LT and SD control leaf senescence. The prediction of the climate change impacts on forest carbon uptake could be improved by incorporating this new autumn phenological model into the terrestrial biosphere models.

Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set

Abstract. The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, AmeriFlux, and Terrestrial Ecosystem Research Network (TERN)/OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the benchmarking of terrestrial biosphere models. However, insufficient quality and frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices, and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40∘ off-nadir. Finally, we offer the community living data sets of pre-processed Earth observation data, where version 1.0 features the MCD43A4/A2 and MxD11A1 MODIS products and Landsat Collection 1 Tier 1 and Tier 2 products in a radius of 2 km around 338 flux sites. The data sets we provide can widely facilitate the integration of activities in the eddy-covariance, remote sensing, and modelling fields.

Evaluation of Historical Wildfires in Tohoku Region Using Satellite-Based High-Fire-Severity Index

Crown fires represent an extreme fire behavior that leads to high fire severity, and dryness plays a vital role in this behavior. Due to the lack of fire severity data in Tohoku, high fire severity was estimated using a satellite-based high-fire-severity index (HFSI). HFSI is the ratio of the identified area of high fire severity sensed using the Landsat-differenced normalized difference vegetation index (dNDVI) to the reported total burnt area. Using the HFSI, only six wildfires could be identified as having high fire severity areas from an evaluation of 55 wildfires with burnt areas greater than 0.1 km2 reported in Tohoku from 1995 to 2017. The low HFSI values computed for these wildfires implied that fireline intensity was not high for crown fires to occur in Tohoku. Additionally, the soil moisture (SM) content for three layers, the surface, root, and recharge zones simulated using a land surface model (the Simple Biosphere Model including Urban Canopy (SiBUC) model), were used to assess the dryness. The highest HFSI value calculated among all wildfires was that of the largest wildfire that ever occurred in Japan in the period between 1995 and 2017, the 2017 Kamaishi wildfire. The conditions before this fire were among the driest of the six wildfires with HFSI values.

Approaches to the definition of potentially exposed groups and potentially exposed populations of biota in the context of solid radioactive waste

A methodology for addressing the biosphere in safety assessments for solid radioactive waste disposal was developed through theme 1 of the IAEA coordinated research project on BIOsphere Modelling and ASSessment (BIOMASS) that ran from 1996 to 2001. This methodology provided guidance on how the biosphere can be addressed in safety assessments for disposal of solid radioactive waste. Since the methodology was developed, it has proven useful and has been widely referenced in assessments in a diversity of contexts encompassing both near-surface and deep geological disposal of solid radioactive waste. The principles that could be adopted for defining potentially exposed groups (PEGs) were an important aspect in the original BIOMASS methodology as the endpoint of an assessment usually includes the evaluation of individual dose or risk to human health. Identification of PEGs and definition of their characteristics are usually made to be consistent with the biosphere system description being developed, acknowledging that due to inherent uncertainties in projecting future human behaviour, the biosphere models adopted for assessing safety of a disposal system can only be illustrative. Since the publication of the original BIOMASS methodology, consideration has been extended to include potentially exposed populations of biota (PEPs), in the context of dose assessment and protection of the environment. Considering the need for the development of transfer pathways from a source term to an end point (for either PEGs or PEPs), the exposure modes that may occur and those to be assessed quantitatively should be identified. Within an expert working group (WG6) of the second phase of the IAEA coordinated project Modelling and Data for Radiological Impact Assessments (MODARIA II), the experience of participating organisations has been collected on topics associated with the definition of PEGs and PEPs using a questionnaire. The objective of the questionnaire was to review the current status and on-going discussions on the handling of issues related to definitions of PEGs and PEPs as an input to the development of biosphere models for assessing radiological impacts on human health and the environment. The answers received to the questionnaire provided a clear overview of the progress that has been made since the original BIOMASS methodology was published, together with the lessons learned from the application of that methodology in the development of safety cases. This paper summarises the questionnaire responses in five subject areas: (1) environment of the PEGs and its evolution; (2) linking the choice of PEGs to these environments; (3) food habits and consumption rates; (4) populations of non-human biota (PEPs) and (5) national and international regulations and guidance. We illustrate how the results of the questionnaire have been used to enhance the original BIOMASS methodology (IAEA Enhanced BIOMASS Methodology Report in press).

Soil carbon and microbes in the warming tropics

Abstract Climate warming could destabilise the Earth's largest terrestrial store of reactive carbon (C), by accelerating the decomposition of soil organic matter. A third of that C store resides in the tropics. The potential for tropical soils to sequester C, or to act as an additional source of CO2, will depend on the balance of C inputs and outputs, mediated by the response of soil microbial communities and their activity to perturbation. We review the impact of warming on microbial communities and C storage in humid tropical forest soils over multiple time‐scales. Recent in situ experiments indicate high sensitivity of tropical forest soil C mineralisation to warming in the short term. However, whether this will translate into long‐term soil C decline remains unclear. At decadal time‐scales, high sensitivity of soil C mineralisation to warming is consistent with the correlation between the inter‐annual variation in the tropical land surface temperature and atmospheric CO2 growth rate, and with simulations using the Carnegie‐Ames‐Stanford Approach biosphere model. This observed sensitivity may further contribute to climatic change over millennial time‐scales, suggested by radiocarbon dating of organic matter in river basins showing a twofold acceleration in tropical soil C release during the late‐glacial warming period. However, counter to this evidence, long‐term stability of tropical soil C is suggested by observed steady‐state soil C turnover across temperature gradients with elevation, and by the presence of C in tropical soils that pre‐dates the Holocene Thermal Maximum and late‐glacial warming periods. To help reconcile these recent experimental findings and long‐term observations, we propose mechanisms to explain tropical soil C and microbial responses to warming across multiple time‐scales. Combined in situ experimental and monitoring approaches—large‐scale and cross‐site—are urgently needed to resolve the interplay of these mechanisms across spatial and temporal scales, to shape a better understanding of the relationship between soil microbes and C storage in tropical soils. Read the free Plain Language Summary for this article on the Journal blog.

Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?

AbstractThe Global Carbon Project estimates that the terrestrial biosphere has absorbed about one‐third of anthropogenic CO2 emissions during the 1959–2019 period. This sink‐estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation‐based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter‐model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.

Tree biomass allocation differs by mycorrhizal association

Tree biomass allocation to leaves, roots, and wood affects the residence time of carbon in forests, with potentially dramatic implications for ecosystem carbon storage. However, drivers of tree biomass allocation remain poorly quantified. Using a combination of global data sets, we tested the relative importance of climate, leaf habit, and tree mycorrhizal associations on biomass allocation. We show that trees that associate with arbuscular mycorrhizal (AM) fungi allocate roughly 4% more of their biomass to root tissue than trees that associate with ectomycorrhizal (ECM) fungi. Further, the effect of mycorrhizal association on root biomass allocation was greater than that of climate and similar in magnitude to that of leaf habit (evergreen vs. deciduous). These patterns in whole-plant biomass allocation are likely due to differences in carbon investment toward root versus fungal tissues, where trees with AM fungi favor root production while trees with ECM fungi favor fungal tissue production. These results suggest that considering tree mycorrhizal associations could improve our understanding of ecosystem carbon storage in terrestrial biosphere models: specifically, that greater within-tree allocation to root biomass in AM-associated tree species may contribute to stable soil carbon pools in forests dominated by AM fungi.

Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems

Abstract. 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 both the immediate and 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 (such as delays between rainfall and peak plant water content or between a precipitation deficit and down-regulation of productivity) have largely been overlooked in the development of terrestrial biosphere 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 12 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). By focussing our analysis on a single continent (and predominately on a single genus), we reduced the degrees of variation between each site, providing a novel chance to explore the unique characteristics that might drive the importance of memory. Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. λE was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.93. When considered at a continental scale, both fluxes were more predictable when memory effects (expressed as lagged climate predictors) were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. Consistent with prior studies, the importance of environmental memory in predicting fluxes increased as site water availability declined (ρ=-0.73, p<0.01 for NEE, ρ=-0.67, p<0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a model of k-means clustering plus regression 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.

Range shifts in a foundation sedge potentially induce large Arctic ecosystem carbon losses and gains

Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge (Eriophorum vaginatum) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ∼0.97 tussocks per m2 over the past ∼38 years on Alaska’s North Slope from ∼1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. −0.8 tussocks per m2 and −85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m2 and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlight the need for better representation of this species in TBMs.

Influence of plant ecophysiology on ozone dry deposition: comparing between multiplicative and photosynthesis-based dry deposition schemes and their responses to rising CO<sub>2</sub> level

Abstract. Dry deposition is a key process for surface ozone (O3) removal. Stomatal uptake is a major component of O3 dry deposition, which is parameterized differently in current land surface models and chemical transport models. We developed and used a standalone terrestrial biosphere model, driven by a unified set of prescribed meteorology, to evaluate two widely used dry deposition modeling frameworks, Wesely (1989) and Zhang et al. (2003), with different configurations of stomatal resistance: (1) the default multiplicative method in the Wesely scheme (W89) and Zhang et al. (2003) scheme (Z03), (2) the traditional photosynthesis-based Farquhar–Ball–Berry (FBB) stomatal algorithm, and (3) the Medlyn stomatal algorithm (MED) based on optimization theory. We found that using the FBB stomatal approach that captures ecophysiological responses to environmental factors, especially to water stress, can generally improve the simulated dry deposition velocities compared with multiplicative schemes. The MED stomatal approach produces higher stomatal conductance than FBB and is likely to overestimate dry deposition velocities for major vegetation types, but its performance is greatly improved when spatially varying slope parameters based on annual mean precipitation are used. Large discrepancies were also found in stomatal responses to rising CO2 levels from 390 to 550 ppm: the multiplicative stomatal method with an empirical CO2 response function produces reduction (−35 %) in global stomatal conductance on average much larger than that with the photosynthesis-based stomatal method (−14 %–19 %). Our results show the potential biases in O3 sink caused by errors in model structure especially in the Wesely dry deposition scheme and the importance of using photosynthesis-based representation of stomatal resistance in dry deposition schemes under a changing climate and rising CO2 concentration.

Skillful Seasonal Forecasts of Land Carbon Uptake in Northern Mid‐ and High Latitudes

AbstractHere we present a first look at the Gross Primary Production (GPP) forecast skill levels achievable with a state‐of‐the‐art subseasonal‐to‐seasonal (S2S) forecast system. Using NASA's retrospective S2S ensemble forecast in conjunction with a terrestrial biosphere model, and using an independent, remote sensing‐based data set for validation, we demonstrate an ability to accurately forecast spring‐summer carbon uptake at multi‐month leads. Averaged across mid‐ and high latitudes of the Northern Hemisphere land, the GPP forecast initialized on January 1 produces statistically significant skill through summer. The skill achieved, however, is spatially variable, with some regions appearing to extract skill from accurate forecasts of snowpack removal and others extracting skill from the initialization of carbon and nitrogen states. Our results reveal some heretofore unexplored facets of climate predictability and provide a look at what might be possible with future S2S forecast systems that are fully integrated with biogeochemical cycles.

Safety assessments undertaken using the BIOMASS methodology: lessons learnt and methodological enhancements

The International Atomic Energy Agency has coordinated an international project addressing enhancements of methods for modelling in post-closure safety assessments of solid radioactive waste disposal. The project used earlier published work from the IAEA biosphere modelling and assessment (BIOMASS) project to further develop methods and techniques. The task was supported by a parallel on-going project within the BIOPROTA forum. The output from the project is described in detail in a forthcoming IAEA report. Here an overview of the work is given to provide researchers in the broader fields of radioecology and radioactive waste disposal with a summarised review of the enhanced BIOMASS methodology and the work that has been undertaken during the project. It is hoped that such dissemination will support and promote integrated understanding and coherent treatment of the biosphere component within the overall assessment process. The key activities undertaken in the project were: review and identification of those parts of the original BIOMASS methodology that needed enhancement, discussions on lessons learned from applying the BIOMASS method, using real examples to assess the methodology and its usefulness, and writing of those parts of the methodology that were considered could benefit from refinement or for which new guidance was required to take account of scientific developments. The work has shown that the overall approach in the original BIOMASS methodology has proven sound. However, the enhanced version clarifies the need for an iterative and holistic approach with system understanding central to the approach. Specifically, experience, especially in site-specific contexts, has emphasised that adequate system understanding is essential in underpinning safety assessments for radioactive waste disposal. The integral role of the biosphere within safety assessment is also emphasised in the enhanced methodology.

CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates

Abstract. Land–atmosphere carbon and water exchanges have large uncertainty in terrestrial biosphere models (TBMs). Using observations to reduce TBM structural and parametric errors and uncertainty is a critical priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARbon DAta–MOdel fraMework (CARDAMOM) have yielded key insights into ecosystem carbon and water cycling. CARDAMOM estimates parameters for an associated TBM of intermediate complexity (Data Assimilation Linked Ecosystem Carbon – DALEC). These CARDAMOM analyses – informed by co-located C​​​​​​​ and H2O flux observations – have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. CARDAMOM and DALEC have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints. However, so far there has been no concerted effort to globally and systematically validate CARDAMOM performance across individual model–data fusion configurations. Here we use the FLUXNET 2015 dataset – an ensemble of 200+ eddy covariance flux tower sites – to formulate a concerted benchmarking framework for CARDAMOM carbon (photosynthesis and net C exchange) and water (evapotranspiration) flux estimates (CARDAMOM-FluxVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET 2015 photosynthesis, net CO2 exchange, and evapotranspiration estimates. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) individual land-cover types, (ii) monthly, annual, and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system – along with the CARDAMOM driver files provided – can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development, and testing of new CARDAMOM model structures.

Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality

<p indent="0mm">Enhancing the terrestrial ecosystem carbon sink (referred to as terrestrial C sink) is an important way to slow down the continuous increase in atmospheric carbon dioxide (CO₂) concentration and to achieve carbon neutrality target. To better understand the characteristics of terrestrial C sinks and their contribution to carbon neutrality, this review summarizes major progress in terrestrial C budget researches during the past decades, clarifies spatial patterns and drivers of terrestrial C sources and sinks in China and around the world, and examines the role of terrestrial C sinks in achieving carbon neutrality target. According to recent studies, the global terrestrial C sink has been increasing from a source of (−0.2±0.9) Pg C yr⁻¹ (1 Pg=<sc>10¹⁵ g)</sc> in the 1960s to a sink of (1.9±1.1) Pg C yr⁻¹ in the 2010s. By synthesizing the published data, we estimate terrestrial C sink of 0.20–0.25 Pg C yr⁻¹ in China during the past decades, and predicted it to be 0.15–0.52 Pg C yr⁻¹ by 2060. The terrestrial C sinks are mainly located in the mid- and high latitudes of the Northern Hemisphere, while tropical regions act as a weak C sink or source. The C balance differs much among ecosystem types: forest is the major C sink; shrubland, wetland and farmland soil act as C sinks; and whether the grassland functions as C sink or source remains unclear. Desert might be a C sink, but the magnitude and the associated mechanisms are still controversial. Elevated atmospheric CO₂ concentration, nitrogen deposition, climate change, and land cover change are the main drivers of terrestrial C sinks, while other factors such as fires and aerosols would also affect ecosystem C balance. The driving factors of terrestrial C sink differ among regions. Elevated CO₂ concentration and climate change are major drivers of the C sinks in North America and Europe, while afforestation and ecological restoration are additionally important forcing factors of terrestrial C sinks in China. For future studies, we recommend the necessity for intensive and long-term ecosystem C monitoring over broad geographic scale to improve terrestrial biosphere models for accurately evaluating terrestrial C budget and its dynamics under various climate change and policy scenarios.

Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality.

Enhancing the terrestrial ecosystem carbon sink (referred to as terrestrial C sink) is an important way to slow down the continuous increase in atmospheric carbon dioxide (CO2) concentration and to achieve carbon neutrality target. To better understand the characteristics of terrestrial C sinks and their contribution to carbon neutrality, this review summarizes major progress in terrestrial C budget researches during the past decades, clarifies spatial patterns and drivers of terrestrial C sources and sinks in China and around the world, and examines the role of terrestrial C sinks in achieving carbon neutrality target. According to recent studies, the global terrestrial C sink has been increasing from a source of (-0.2±0.9) Pg C yr-1 (1 Pg=1015 g) in the 1960s to a sink of (1.9±1.1) Pg C yr-1 in the 2010s. By synthesizing the published data, we estimate terrestrial C sink of 0.20-0.25 Pg C yr-1 in China during the past decades, and predict it to be 0.15-0.52 Pg C yr-1 by 2060. The terrestrial C sinks are mainly located in the mid- and high latitudes of the Northern Hemisphere, while tropical regions act as a weak C sink or source. The C balance differs much among ecosystem types: forest is the major C sink; shrubland, wetland and farmland soil act as C sinks; and whether the grassland functions as C sink or source remains unclear. Desert might be a C sink, but the magnitude and the associated mechanisms are still controversial. Elevated atmospheric CO2 concentration, nitrogen deposition, climate change, and land cover change are the main drivers of terrestrial C sinks, while other factors such as fires and aerosols would also affect ecosystem C balance. The driving factors of terrestrial C sink differ among regions. Elevated CO2 concentration and climate change are major drivers of the C sinks in North America and Europe, while afforestation and ecological restoration are additionally important forcing factors of terrestrial C sinks in China. For future studies, we recommend the necessity for intensive and long term ecosystem C monitoring over broad geographic scale to improve terrestrial biosphere models for accurately evaluating terrestrial C budget and its dynamics under various climate change and policy scenarios.

Temperature sensitivity of woody nitrogen fixation across species and growing temperatures.

The future of the land carbon sink depends on the availability of nitrogen (N)1,2 and, specifically, on symbiotic N fixation3-8, which can rapidly alleviate N limitation. The temperature response of symbiotic N fixation has been hypothesized to explain the global distribution of N-fixing trees9,10 and is a key part of some terrestrial biosphere models (TBMs)3,7,8, yet there are few data to constrain the temperature response of symbiotic N fixation. Here we show that optimal temperatures for N fixation in four tree symbioses are in the range 29.0-36.9 °C, well above the 25.2 °C optimum currently used by TBMs. The shape of the response to temperature is also markedly different to the function used by TBMs (asymmetric rather than symmetric). We also show that N fixation acclimates to growing temperature (hence its range of optimal temperatures), particularly in our two tropical symbioses. Surprisingly, optimal temperatures were 5.2 °C higher for N fixation than for photosynthesis, suggesting that plant carbon and N gain are decoupled with respect to temperature. These findings may help explain why N-fixing tree abundance is highest where annual maximum temperatures are >35 °C (ref. 10) and why N-fixing symbioses evolved during a warm period in the Earth's history11,12. Everything else being equal, our findings indicate that climate warming will probably increase N fixation, even in tropical ecosystems, in direct contrast to past projections8.

Late-day measurement of excised branches results in uncertainty in the estimation of two stomatal parameters derived from response curves in Populus deltoides Bartr.×Populus nigra L.

Many terrestrial biosphere models depend on an understanding of the relationship between stomatal conductance and photosynthesis. However, unlike the measurement of photosynthetic parameters, such as the maximum carboxylation capacity, where standard methods (e.g., CO2 response or ACi curves) are widely accepted, a consensus method for empirically measuring parameters representing stomatal response has not yet emerged. Most models of stomatal response to environment represent stomatal conductance as being bounded by a lower intercept parameter (g0), and linearly scaled based on a multivariate term described by the stomatal slope parameter (g1). Here we employ the widely used Unified Stomatal Optimization model, to test whether g1 and g0 parameters are impacted by the choice of measurement method, either on an intact branch or a cut branch segment stored in water. We measured paired stomatal response curves on intact and excised branches of a hybrid poplar clone (Populus deltoides Bartr.×Populus nigra L. OP367), measured twice over a diurnal period. We found that predawn branch excision did not significantly affect measured g0 and g1 when measured within 4 h of excision. Measurement in the afternoon resulted in significantly higher values of g1 and lower values of g0, with values changing by 55% and 56%, respectively. Excision combined with afternoon measurement resulted in a marked effect on parameter estimates, with g1 increasing 89% from morning to afternoon and a 25% lower g1 for cut branches than those measured in situ. We also show that in hybrid poplar the differences in parameter estimates obtained from plants measured under different conditions can directly impact models of canopy function, reducing modeled transpiration by 18% over a simulated 12.5-h period. Although these results are only for a single isohydric woody species, our findings suggest that stomatal optimality parameters may not remain constant throughout the day.

The Terrestrial Biosphere Model Farm.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ‐GUESS, ORCHIDEE, SiB‐3, and SiB‐CASA. All models were wrapped in a software framework driven with common forcing data, spin‐up, and run protocols specified by the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open‐source, cloud‐based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.