Terrestrial Biosphere Models Research Articles

Photosynthetic temperature responses in leaves and canopies: why temperature optima may disagree at different scales.

Understanding how canopy-scale photosynthesis responds to temperature is of paramount importance for realistic prediction of the likely impact of climate change on forest growth. The effects of temperature on leaf-scale photosynthesis have been extensively documented but data demonstrating the temperature response of canopy-scale photosynthesis are relatively rare, and the mechanisms that determine the response are not well quantified. Here, we compared leaf- and canopy-scale photosynthesis responses to temperature measured in a whole-tree chamber experiment and tested mechanisms that could explain the difference between leaf and crown scale temperature optima for photosynthesis. We hypothesised that 1) there is a large contribution of non-light saturated leaves to total crown photosynthesis; 2) photosynthetic component processes vary vertically through the canopy following the gradient in incident light; and 3) seasonal temperature acclimation of photosynthetic biochemistry has a significant role in determining the overall temperature response of canopy photosynthesis. We tested these hypotheses using three models of canopy radiation interception and photosynthesis parameterized with leaf-level physiological data and estimates of canopy leaf area. Our results identified the influence of non-light saturated leaves as a key determinant of the lower temperature optimum of canopy photosynthesis, which reduced the temperature optimum of canopy photosynthesis by 6-8°C compared to the leaf scale. Further, we demonstrate the importance of accounting for within-canopy variation and seasonal temperature acclimation of photosynthetic biochemistry in determining the magnitude of canopy photosynthesis. Overall, our study identifies key processes that need to be incorporated in terrestrial biosphere models to accurately predict temperature responses of whole-tree photosynthesis.

Different model assumptions about plant hydraulics and photosynthetic temperature acclimation yield diverging implications for tropical forest gross primary production under warming

AbstractTropical forest photosynthesis can decline at high temperatures due to (1) biochemical responses to increasing temperature and (2) stomatal responses to increasing vapor pressure deficit (VPD), which is associated with increasing temperature. It is challenging to disentangle the influence of these two mechanisms on photosynthesis in observations, because temperature and VPD are tightly correlated in tropical forests. Nonetheless, quantifying the relative strength of these two mechanisms is essential for understanding how tropical gross primary production (GPP) will respond to climate change, because increasing atmospheric CO2 concentration may partially offset VPD‐driven stomatal responses, but is not expected to mitigate the effects of temperature‐driven biochemical responses. We used two terrestrial biosphere models to quantify how physiological process assumptions (photosynthetic temperature acclimation and plant hydraulic stress) and functional traits (e.g., maximum xylem conductivity) influence the relative strength of modeled temperature versus VPD effects on light‐saturated GPP at an Amazonian forest site, a seasonally dry tropical forest site, and an experimental tropical forest mesocosm. By simulating idealized climate change scenarios, we quantified the divergence in GPP predictions under model configurations with stronger VPD effects compared with stronger direct temperature effects. Assumptions consistent with stronger direct temperature effects resulted in larger GPP declines under warming, while assumptions consistent with stronger VPD effects resulted in more resilient GPP under warming. Our findings underscore the importance of quantifying the role of direct temperature and indirect VPD effects for projecting the resilience of tropical forests in the future, and demonstrate that the relative strength of temperature versus VPD effects in models is highly sensitive to plant functional parameters and structural assumptions about photosynthetic temperature acclimation and plant hydraulics.

The impacts of modelling prescribed vs. dynamic land cover in a high-CO2 future scenario – greening of the Arctic and Amazonian dieback

Abstract. Terrestrial biosphere models are a key tool in investigating the role played by land surface in the global climate system. However, few models simulate the geographic distribution of biomes dynamically, opting instead to prescribe them using remote sensing products. While prescribing land cover still allows for the simulation of the impacts of climate change on vegetation growth and the impacts of land use change, it prevents the simulation of climate-change-driven biome shifts, with implications for the projection of future terrestrial carbon sink. Here, we isolate the impacts of prescribed vs. dynamic land cover implementations in a terrestrial biosphere model. We first introduce a new framework for evaluating dynamic land cover (i.e., the spatial distribution of plant functional types across the land surface), which can be applied across terrestrial biosphere models alongside standard benchmarking of energy, water, and carbon cycle variables in model intercomparison projects. After validating simulated land cover, we then show that the simulated terrestrial carbon sink differs significantly between simulations with dynamic vs. prescribed land cover for a high-CO2 future scenario. This is because of important range shifts that are only simulated when dynamic land cover is implemented: tree expansion into the Arctic and Amazonian transition from forest to grassland. In particular, the projected change in net land–atmosphere CO2 flux at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover. Our results illustrate the importance of climate-change-driven biome shifts for projecting future terrestrial carbon sink.

The Modeled Seasonal Cycles of Surface N2O Fluxes and Atmospheric N2O

AbstractNitrous oxide (N2O) is a greenhouse gas and stratospheric ozone‐depleting substance with large and growing anthropogenic emissions. Previous studies identified the influx of N2O‐depleted air from the stratosphere to partly cause the seasonality in tropospheric N2O (aN2O), but other contributions remain unclear. Here, we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N2O Model Intercomparison Project with tropospheric transport modeling to simulate aN2O at eight remote air sampling sites for modern and pre‐industrial periods. Models show general agreement on the seasonal phasing of zonal‐average N2O fluxes for most sites, but seasonal peak‐to‐peak amplitudes differ several‐fold across models. The modeled seasonal amplitude of surface aN2O ranges from 0.25 to 0.80 ppb (interquartile ranges 21%–52% of median) for land, 0.14–0.25 ppb (17%–68%) for ocean, and 0.28–0.77 ppb (23%–52%) for combined flux contributions. The observed seasonal amplitude ranges from 0.34 to 1.08 ppb for these sites. The stratospheric contributions to aN2O, inferred by the difference between the surface‐troposphere model and observations, show 16%–126% larger amplitudes and minima delayed by ∼1 month compared to Northern Hemisphere site observations. Land fluxes and their seasonal amplitude have increased since the pre‐industrial era and are projected to grow further under anthropogenic activities. Our results demonstrate the increasing importance of land fluxes for aN2O seasonality. Considering the large model spread, in situ aN2O observations and atmospheric transport‐chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting carbon‐nitrogen cycles under ongoing global warming.

Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0

Abstract. Land ecosystems are important sources and sinks of atmospheric components. In turn, air pollutants affect the exchange rates of carbon and water fluxes between ecosystems and the atmosphere. However, these biogeochemical processes are usually not well presented in Earth system models, limiting the explorations of interactions between land ecosystems and air pollutants from regional to global scales. Here, we develop and validate the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) by upgrading the Yale Interactive Terrestrial Biosphere Model with process-based water cycles, fire emissions, wetland methane (CH4) emissions, and trait-based ozone (O3) damage. Within iMAPLE, soil moisture and temperature are dynamically calculated based on the water and energy balance in soil layers. Fire emissions are dependent on dryness, lightning, population, and fuel load. Wetland CH4 is produced but consumed through oxidation, ebullition, diffusion, and plant-mediated transport. The trait-based scheme unifies O3 sensitivity of different plant functional types (PFTs) with the leaf mass per area. Validations show correlation coefficients (R) of 0.59–0.86 for gross primary productivity (GPP) and 0.57–0.84 for evapotranspiration (ET) across the six PFTs at 201 flux tower sites and yield an average R of 0.68 for CH4 emissions at 44 sites. Simulated soil moisture and temperature match reanalysis data with high R above 0.86 and low normalized mean biases (NMBs) within 7 %, leading to reasonable simulations of global GPP (R=0.92, NMB=1.3 %) and ET (R=0.93, NMB=-10.4 %) against satellite-based observations for 2001–2013. The model predicts an annual global area burned of 507.1 Mha, close to the observations of 475.4 Mha with a spatial R of 0.66 for 1997–2016. The wetland CH4 emissions are estimated to be 153.45 Tg [CH4] yr−1 during 2000–2014, close to the multi-model mean of 148 Tg [CH4] yr−1. The model also shows reasonable responses of GPP and ET to the changes in diffuse radiation and yields mean O3 damage of 2.9 % to global GPP. iMAPLE provides an advanced tool for studying the interactions between land ecosystems and air pollutants.

Developing novel ensemble models for predicting soil hydraulic properties in China’s arid region

Accurately quantifying the mass (water, nutrients, and carbon) and energy exchange processes between the Earth’s atmosphere, biosphere, and lithosphere requires the accurate parameterization of soil hydraulic properties (SHPs) and their spatial heterogeneity. Because direct measurements of SHPs are difficult, time-consuming, and impossible at larger spatial scales, various pedotransfer functions (PTFs) have been developed in the last few decades, providing divergent estimates of SHPs from readily measurable variables. However, existing PTFs are mostly developed for specific regions and may not be suitable for other pedoclimatic conditions. Here, PTFs were developed using multiple machine-learning algorithms to estimate SHPs and examined the weaknesses and strengths of each method in estimating the average and variability of SHPs across China’s arid region. The optimal PTFs were applied to a 1 × 1 km2 regional map of texture and bulk density, thus producing maps of the saturated hydraulic conductivity (Ks), the parameters of the van Genuchten (VG) formulation, field capacity (θfc), wilting point (θwp), plant available water (θpa), and soil macroporosity (ϕm) in the 0–2 m soil profile throughout the region. The results indicate that the ensemble model with the averaging method (EMA) is the most robust for estimating all SHPs. The EMA-PTFs for Ks yielded the best performance for sand textures, followed by sandy loam, loam, silty loam, silty clay loam, loamy sand, and clay loam textures. There were no significant differences in estimating soil water retention curve parameters among the different soil texture classes. Using the same observed data set, we demonstrated that the new EMA-PTFs outperformed those of existing PTFs, such as Rosetta and HYPRES, with RMSE values decreasing by 25–83 % depending on the SHPs. Furthermore, our SHP datasets exhibited significantly deeper soil profiles and higher accuracy than other available regional and global SHP products. The VG retention parameter α shows the greatest variation vertically throughout the 0–2 m soil profile, followed by ln(Ks), θr, θpa, θfc, θwp, ϕm, θs and n. All SHPs exhibit much lower variability in the desert than in other areas, likely due to the homogeneous particle size distribution of desert areas. This study provides more accurate SHP estimates in China’s arid region than the existing SHP products by developing advanced PTFs and highlights the inconsistency in SHPs among different global or regional products; the uncertainties induced by PTFs should thus be considered in future terrestrial biosphere modeling.

The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application

Abstract. The interactions between the terrestrial biosphere, atmospheric chemistry, and climate involve complex feedbacks that have traditionally been modeled separately. We present a new framework that couples the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic plant-chemistry model, with the RegCM-Chem model. RegCM-Chem–YIBs integrates meteorological variables and atmospheric chemical composition from RegCM-Chem with land surface parameters from YIBs. The terrestrial carbon flux calculated by YIBs is fed back into RegCM-Chem interactively, thereby representing the interactions between fine particulate matter (PM2.5), ozone (O3), and carbon dioxide (CO2). For testing purposes, we carry out a 1-year simulation (2016) at a 30 km horizontal resolution over East Asia with RegCM-Chem–YIBs. The model accurately captures the spatio-temporal distribution of climate, chemical composition, and ecological parameters. In particular, the estimated O3 and PM2.5 are consistent with ground observations, with correlation coefficients (R) of 0.74 and 0.65, respectively. The simulated CO2 concentration is consistent with observations from six sites (R ranged from 0.89 to 0.97) and exhibits a similar spatial pattern when compared with carbon assimilation products. RegCM-Chem–YIBs produces reasonably good gross primary productivity (GPP) and net primary productivity (NPP), showing seasonal and spatial distributions consistent with satellite observations, and mean biases (MBs) of 0.13 and 0.05 kg C m−2 yr−1. This study illustrates that RegCM-Chem–YIBs is a valuable tool to investigate coupled interactions between the terrestrial carbon cycle, atmospheric chemistry, and climate change at a higher resolution on a regional scale.

Optimizing seasonally variable photosynthetic parameters based on joint carbon and water flux constraints

Terrestrial biosphere models (TBMs) often adopt the Farquhar biochemical model coupled with the Ball-Berry stomatal conductance (gs) model to simulate ecosystem carbon and water fluxes. The parameters m, representing the sensitivity of gs to the photosynthetic rate, and Vcmax25, representing the leaf photosynthetic capacity, are two pivotal parameters but the two main sources of uncertainties in TBM simulations. The temporal variations of m in TBMs are still elusive, due to the lack of direct observations. It also remains unclear how accurate estimates of m and Vcmax25 can improve the simulations of carbon and water fluxes. In this study, we used a Bayesian parameter optimization approach to estimate seasonally varying m and Vcmax25 from eddy covariance observations in a mixed forest stand at the Borden Forest Research Station located in southern Ontario, Canada and used in-situ observations of m and Vcmax25 for validation. Three strategies were tested for optimizing m and Vcmax25, including the carbon, water, and carbon-water coupling scenarios. m and Vcmax25 optimized from carbon-water coupling constraints shows best correlations with the measured m (R2 = 0.70) and Vcmax25 (R2 = 0.70). By incorporating optimized m and Vcmax25with seasonal variations, we found considerable improvements in the estimated gross primary productivity (GPP) and evapotranspiration (ET) compared with constant m and Vcmax25, with R2 increasing from 0.78 to 0.85 for GPP, from 0.65 to 0.71 for ET and RMSE reducing from 2.579 g C m−2d−1 to 2.038 g C m−2d−1 for GPP, from 1.151 mm d−1 to 0.137 mm d−1 for ET. This study proposes an effective approach to retrieve m and Vcmax25 for TBMs and demonstrates the efficacy of incorporating seasonally variable m and Vcmax25 for reducing the uncertainties in GPP and ET simulations, which supports accurate quantifications of land-atmosphere exchanges.

Optimal representation of spring phenology on photosynthetic productivity across the northern hemisphere forests

Accurate simulation of the onset of spring is crucial for predicting the photosynthetic productivity of forest ecosystems. Nonetheless, the potential of phenology simulations on predictions of forest gross primary productivity (GPP) remains poorly understood, with previous studies generally focused on predicting phenology dates themselves or on limited scales. Here, we constructed a framework through the critical process of leaf growth to couple a terrestrial biosphere model—FORCCHN2 with five widely used phenology models, including one-phase (involving heat forcing), two-phase (considering chilling demand), and multi-phase models (integrating photoperiod effects) through the critical process of leaf growth. We evaluated the GPP performance from the multiple forest biomes in 74 eddy covariance (EC) sites and compared the total trends to satellite observations across the northern hemisphere forests. The predictions of the five models could reproduce 62.41–67.24 % variations of the GPP observations in all EC sites. In the deciduous broadleaf forest sites, we found the two-phase and multi-phase coupled models performed better than the one-phase coupled model. Chilling may play an essential role in controlling photosynthetic activity in deciduous broadleaf forests. Moreover, the multi-phase coupled model integrated photoperiod effects performed best at capturing GPP changes of the whole northern hemisphere forests. The predictions also showed the GPP in the northern hemisphere ranged from 21.68 to 26.06 Pg C year−1, and the total GPP showed an increasing trend. This study provides local and regional available evidence for the impact of phenology on photosynthesis with climate warming. The results highlight the necessity for enhancing understanding of the phenology factors on GPP and integrating the appropriate phenology representation to improve predictions of photosynthetic productivity for forests, especially in prediction to large scales and long-term trends.

Modelled forest ecosystem carbon–nitrogen dynamics with integrated mycorrhizal processes under elevated CO2

Abstract. Almost 95 % of all terrestrial plant species form symbioses with mycorrhizal fungi that mediate plant–soil interactions: mycorrhizae facilitate plant nitrogen (N) acquisition and are, therefore, vital for plant growth, but they also build a pathway for plant-assimilated carbon (C) into the rhizosphere. Therefore, mycorrhizae likely play an important role in shaping the response of ecosystems to environmental changes such as rising atmospheric carbon dioxide (CO2) concentrations, which can increase plant N demand and the transfer of plant C assimilation to the soil. While the importance of mycorrhizal fungi is widely recognised, they are rarely represented in current terrestrial biosphere models (TBMs) explicitly. Here, we present a novel, dynamic plant–mycorrhiza–soil model as part of the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. This new model is based on mycorrhizal functional types that either actively mine soil organic matter (SOM) for N or enhance soil microbial activity through increased transfer of labile C into the rhizosphere, thereby (passively) priming SOM decomposition. Using the Duke Free-Air CO2 Enrichment (FACE) experiment, we show that mycorrhizal fungi can have important effects on projected SOM turnover and plant nutrition under ambient as well as elevated-CO2 treatments. Specifically, we find that including enhanced active mining of SOM for N in the model allows one to more closely match the observations with respect to observed decadal responses of plant growth, plant N acquisition and soil C dynamics to elevated CO2, whereas a simple enhancement of SOM turnover by increased below-ground C transfer of mycorrhizae is unable to replicate the observed responses. We provide an extensive parameter uncertainty study to investigate the robustness of our findings with respect to model parameters that cannot readily be constrained by observations. Our study points to the importance of implementing mycorrhizal functionalities in TBMs as well as to further observational needs to better constrain mycorrhizal models and to close the existing major knowledge gaps in actual mycorrhizal functioning.

Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations

Abstract. Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool for providing benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these models often estimate GPP and ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics at the diurnal (hourly) scales, and prescribe some essential parameters (i.e., the Ball–Berry slope (m) and the maximum carboxylation rate at 25 °C (Vcmax25)) as constant, inducing uncertainties in the estimates of GPP and ET. In this study, we present hourly estimations of global GPP and ET datasets at a 0.25° resolution from 2001 to 2020 simulated with a widely used diagnostic TBM – the Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance observations and machine learning approaches to derive and upscale the seasonally varied m and Vcmax25 for carbon and water fluxes. The estimated hourly GPP and ET are validated against flux observations, remote sensing, and machine learning-based estimates across multiple spatial and temporal scales. The correlation coefficients (R2) and slopes between hourly tower-measured and modeled fluxes are R2=0.83, regression slope =0.92 for GPP, and R2=0.72, regression slope =1.04 for ET. At the global scale, we estimated a global mean GPP of 137.78±3.22 Pg C yr−1 (mean ± 1 SD) with a positive trend of 0.53 Pg C yr−2 (p<0.001), and an ET of 89.03±0.82×103 km3 yr−1 with a slight positive trend of 0.10×103 km3 yr−2 (p<0.001) from 2001 to 2020. The spatial pattern of our estimates agrees well with other products, with R2=0.77–0.85 and R2=0.74–0.90 for GPP and ET, respectively. Overall, this new global hourly dataset serves as a “handshake” among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global GPP and ET with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles. The hourly GPP and ET estimates are available at https://doi.org/10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) and the accumulated daily datasets are available at https://doi.org/10.57760/sciencedb.ecodb.00165 (Leng et al., 2023b).

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Evaluation of Leaf‐To‐Canopy Upscaling Approaches for Simulating Canopy Carbonyl Sulfide Uptake and Gross Primary Productivity

AbstractCarbonyl sulfide (COS) measurements provide an important mechanism for quantifying the terrestrial carbon and water cycles. Terrestrial photosynthesis can be inferred from vegetative uptake of COS due to the shared diffusion pathway of COS and CO2 into the chloroplasts. However, the efficacy of different leaf‐to‐canopy upscaling approaches for estimating plant COS uptake and photosynthesis at the canopy scale remains uncertain. In this study, the daytime canopy COS uptake rate (Fcosc) and gross primary productivity (GPP) were simulated using big leaf (BL), two big leaf (TBL) and two leaf (TL) upscaling approaches in the Boreal Ecosystem Productivity Simulator terrestrial biosphere model for two forest sites. We found that TL and TBL performed better than BL in estimating GPP as evaluated by the eddy covariance derived GPP at the studied sites. BL overestimated GPP and canopy stomatal conductance to COS (Gs_cos), resulting from the overestimation of canopy intercepted solar radiation. The positive biases of GPP by TBL could be attributed to the positive biases in GPP of shaded leaves by TBL. The results in this study demonstrate the improved accuracy in using TL for estimating GPP at the canopy scale. Improved leaf‐to‐canopy upscaling approach in terrestrial biosphere models will improve the accuracy of canopy COS uptake and GPP estimation on regional to global scales.

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM‐FATES‐CNP)

AbstractWe present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon‐nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre‐existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant‐soil dynamics for a large number of size‐ and functional‐type‐resolved plant cohorts within a time‐since‐disturbance‐resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high‐light‐availability canopy positions allocate more carbon to fine roots than plants in low‐light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon‐nutrient coupling with vegetation demography within Earth system models.

Using Free Air CO2 Enrichment data to constrain land surface model projections of the terrestrial carbon cycle

Abstract. Predicting the responses of terrestrial ecosystem carbon to future global change strongly relies on our ability to model accurately the underlying processes at a global scale. However, terrestrial biosphere models representing the carbon and nitrogen cycles and their interactions remain subject to large uncertainties, partly because of unknown or poorly constrained parameters. Parameter estimation is a powerful tool that can be used to optimise these parameters by confronting the model with observations. In this paper, we identify sensitive model parameters from a recent version of the ORgainzing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model that includes the nitrogen cycle. These sensitive parameters include ones involved in parameterisations controlling the impact of the nitrogen cycle on the carbon cycle and, in particular, the limitation of photosynthesis due to leaf nitrogen availability. We optimise these ORCHIDEE parameters against carbon flux data collected on sites from the FLUXNET network. However, optimising against present-day observations does not automatically give us confidence in future projections of the model, given that environmental conditions are likely to shift compared to the present day. Manipulation experiments give us a unique look into how the ecosystem may respond to future environmental changes. One such type of manipulation experiment, the Free Air CO2 Enrichment (FACE) experiment, provides a unique opportunity to assess vegetation response to increasing CO2 by providing data under ambient and elevated CO2 conditions. Therefore, to better capture the ecosystem response to increased CO2, we add the data from two FACE sites to our optimisations, in addition to the FLUXNET data. We use data from both CO2 conditions of FACE, which allows us to gain extra confidence in the model simulations using this set of parameters. We find that we are able to improve the magnitude of modelled productivity. Although we are unable to correct the interannual variability fully, we start to simulate possible progressive nitrogen limitation at one of the sites. Using an idealised simulation experiment based on increasing atmospheric CO2 by 1 % yr−1 over 100 years, we find that optimising against only FLUXNET data tends to imply a large fertilisation effect, whereas optimising against FLUXNET and FACE data (with information about nutrient limitation and acclimation of plants) decreases it significantly.

Multiscale assessment of North American terrestrial carbon balance

Abstract. Comparisons of carbon uptake estimates from bottom-up terrestrial biosphere models (TBMs) to top-down atmospheric inversions help assess how well we understand carbon dioxide (CO2) exchange between the atmosphere and terrestrial biosphere. Previous comparisons have shown varying levels of agreement between bottom-up and top-down approaches, but they have almost exclusively focused on large, aggregated scales (e.g., global or continental), providing limited insights into reasons for the mismatches. Here we explore how consistency, defined as the spread in net ecosystem exchange (NEE) estimates within an ensemble of TBMs or inversions, varies with at finer spatial scales ranging from 1∘×1∘ to the continent of North America. We also evaluate how well consistency informs accuracy in overall NEE estimates by filtering models based on their agreement with the variability, magnitude, and seasonality in observed atmospheric CO2 drawdowns or enhancements. We find that TBMs produce more consistent estimates of NEE for most regions and at most scales relative to inversions. Filtering models using atmospheric CO2 metrics causes ensemble spread to decrease substantially for TBMs, but not for inversions. This suggests that ensemble spread is likely not a reliable measure of the uncertainty associated with the North American carbon balance at any spatial scale. Promisingly, applying atmospheric CO2 metrics leads to a set of models with converging flux estimates across TBMs and inversions. Overall, we show that multiscale assessment of the agreement between bottom-up and top-down NEE estimates, aided by regional-scale observational constraints is a promising path towards identifying fine-scale sources of uncertainty and improving both ensemble consistency and accuracy. These findings help refine our understanding of biospheric carbon balance, particularly at scales relevant for informing regional carbon-climate feedbacks.

Evaluate the differences in carbon sink contribution of different ecological engineering projects

China has implemented a series of ecological engineering projects to help achieve the 2060 carbon neutrality target. However, the lack of quantitative research on ecological engineering and the contribution of climate change to terrestrial carbon sinks limits this goal. This study uses robust statistical models combined with multiple terrestrial biosphere models to quantify the impact of China's ecological engineering on terrestrial ecosystem carbon sink trends and their differences according to the difference between reality and nonpractice assumptions. The main conclusions include the following: (1) since 1901, 84% of terrestrial ecosystem carbon sinks in China have shown an increasing trend, and approximately 45% of regional carbon sinks have increased by more than 0.1 g C/m2 every 10 years. (2) Considering the impact of human activities and the implementation of ecological engineering in China, approximately 56% of carbon sinks have improved, and approximately 10% of the regions whose carbon sink growth exceeds 50 g C m−2 yr−1 are mainly in the southeast coastal of China. (3) The carbon sequestration potential and effect of the Sanjiangyuan ecological protection and construction project are better than others, at 1.26 g C m−2 yr−1 and 14.13%, respectively. The Beijing–Tianjin sandstorm source comprehensive control project helps alleviate the reduction in carbon sinks, while the southwest karst rocky desertification comprehensive control project may aggravate the reduction in carbon sinks. This study clarifies the potential of China's different ecological engineering to increase carbon sink potential, and distinguishes and quantifies the contribution of climate and human activity factors to it, which is of great significance to the system management optimization scheme of terrestrial ecosystems and can effectively serve the national carbon neutral strategy.Graphical

Phosphorus limitation on CO2 fertilization effect in tropical forests informed by a coupled biogeochemical model

Tropical forests store more than half of the world's terrestrial carbon (C) pool and account for one-third of global net primary productivity (NPP). Many terrestrial biosphere models (TBMs) estimate increased productivity in tropical forests throughout the 21st century due to CO2 fertilization. However, phosphorus (P) limitations on vegetation photosynthesis and productivity could significantly reduce the CO2 fertilization effect. Here, we used a carbon-nitrogen-phosphorus coupled model (Dynamic Land Ecosystem Model; DLEM-CNP) with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests during 1860–2018. Our model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem productivity. We further compared the simulations from DLEM-CNP, DLEM-CN, and DLEM-C and the results showed that the inclusion of P processes reduced the CO2 fertilization effect on gross primary production (GPP) by 25% and 45%, and net ecosystem production (NEP) by 28% and 41%, respectively, relative to CN-only and C-only models. From the 1860s to the 2010s, the DLEM-CNP estimated that in tropical forests GPP increased by 17%, plant respiration (Ra) increased by 18%, ecosystem respiration (Rh) increased by 13%, NEP increased by 121% per unit area, respectively. Additionally, factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO2 fertilization effect had been offset by 135% due to deforestation from the 1860s to the 2010s. Our study highlights the importance of P limitation on the C cycle and the weakened CO2 fertilization effect resulting from P limitation in tropical forests.

Are Plant Functional Types Fit for Purpose?

AbstractFor over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into “similar” classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies.

Integrating leaf functional traits improves modelled estimates of carbon and water fluxes at a subtropical evergreen conifer forest

Simulations of gross primary productivity (GPP) and evapotranspiration (ET) by terrestrial biosphere models (TBMs) are subject to significant uncertainty, in part due to the spatiotemporal variability in leaf photosynthetic capacity, which is not well represented in models. Recent studies have shown the potential for using leaf chlorophyll content (Chlleaf) to constrain GPP and ET modeling in deciduous vegetation with a strong seasonal phenology. However, little is known about how integrating physiological trait information affects modelled GPP and ET in evergreen plants. In this study, we investigated the feasibility of incorporating Chlleaf and leaf age into a TBM, as a proxy for leaf maximum carboxylation rate at 25 °C (Vcmax25) for improving GPP and ET simulations. Measurements of Chlleaf and Vcmax25 from different leaf age classes (current-year and 1-year-old) for Masson pine and Slash pine species, and leaf area index (LAI) were made in a subtropical Evergreen Needleleaf Forest (ENF) eddy covariance flux tower site. The parameterization of Vcmax25 using combined information on Chlleaf and leaf age considerably reduced the biases in simulated GPP and ET, relative to the cases of i) constant Vcmax25 and ii) Chlleaf based Vcmax25. The largest improvements in GPP and ET simulations were found in growing season (May to August), when monthly absolute errors (AEs) of modeled GPP were ∼40 % reduced, from 120.5 to 71.2 g C m−2 mon−1, with a 25 % decrease of monthly AEs of modeled ET from 52.3 to 39.1 mm mon−1. Chlleaf plays a different role in modelled photosynthesis and transpiration between sunlit and shaded leaves. The modeled water use efficiency (WUE) and light use efficiency (LUE) of the shaded leaves were both higher than those of sunlit leaves. This study presents the newly use of Chlleaf and leaf age as a proxy for improving Vcmax25 modeling at an ENF stand, which highlights the importance of using plant physiological traits and leaf age for improving ecosystem carbon-water coupling simulations.