Search

We 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. Plain Language Summary: This work introduces a new set of nutrient cycling hypotheses incorporated into a terrestrial biosphere model. This includes the cycling of carbon, nitrogen and phosphorus, and focuses mainly on plant acquisition, allocation, and turnover. An analysis shows the model offers reasonable responses to perturbations in parameter constants and variability in climate forcing, considering its design balance between process complexity and parameterization burden. Key Points: The nutrient enabled ELM‐FATES model represents expected pattern responses to nutrient availability and parameter perturbationsThe model has been designed to introduce a reasonably small parameterization burdenThese hypothesis can capture some but not all elements of carbon‐nutrient dynamics and can be further inter‐compared with other hypotheses [ABSTRACT FROM AUTHOR]

Projecting the dynamics and functioning of the biosphere requires a holistic consideration of whole‐ecosystem processes. However, biases toward leaf, canopy, and soil modeling since the 1970s have constantly left fine‐root systems being rudimentarily treated. As accelerated empirical advances in the last two decades establish clearly functional differentiation conferred by the hierarchical structure of fine‐root orders and associations with mycorrhizal fungi, a need emerges to embrace this complexity to bridge the data‐model gap in still extremely uncertain models. Here, we propose a three‐pool structure comprising transport and absorptive fine roots with mycorrhizal fungi (TAM) to model vertically resolved fine‐root systems across organizational and spatial–temporal scales. Emerging from a conceptual shift away from arbitrary homogenization, TAM builds upon theoretical and empirical foundations as an effective and efficient approximation that balances realism and simplicity. A proof‐of‐concept demonstration of TAM in a big‐leaf model both conservatively and radically shows robust impacts of differentiation within fine‐root systems on simulating carbon cycling in temperate forests. Theoretical and quantitative support warrants exploiting its rich potentials across ecosystems and models to confront uncertainties and challenges for a predictive understanding of the biosphere. Echoing a broad trend of embracing ecological complexity in integrative ecosystem modeling, TAM may offer a consistent framework where modelers and empiricists can work together toward this grand goal. [ABSTRACT FROM AUTHOR]

Perennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. Few Earth System Models (ESMs) represent such crops, however. In this study, we expand the Energy Exascale Earth System Land Model to include perennial bioenergy crops with a high potential for mitigating climate change. We focus on high‐productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies five parameters associated with phenology, carbon/nitrogen allocation, stomatal conductance, and maintenance respiration as the most sensitive parameters for carbon and energy fluxes. We calibrated and validated the model against observations and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The validated model represents the latent heat flux fairly well, but sensible heat flux for miscanthus is not well captured. Finally, we validated the model against observed leaf area index (LAI) and harvest amount and found modeled LAI captured observed seasonality, although the model underestimates LAI and harvest amount. This work provides a foundation for future ESM analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger Earth system, and sets the stage for studying the impact of future biofuel expansion on climate and terrestrial systems. Plain Language Summary: Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Land Model to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion. Key Points: The study expands the Energy Exascale Earth System Land Model to include perennial bioenergy crops miscanthus and switchgrassCalibrated model captures the observed seasonality and the magnitude of carbon and energy fluxesThis study provides the foundation for future research examining the impact of perennial bioenergy crop expansion [ABSTRACT FROM AUTHOR]

Inland waters serve as important hydrological connections between the terrestrial landscape and oceans but are often overlooked in global carbon (C) budgets and Earth System Models. Terrestrially derived C entering inland waters from the watershed can be transported to oceans but over 83% is either buried in sediments or emitted to the atmosphere before reaching oceans. Anthropogenic pressures such as climate and landscape changes are altering the magnitude of these C fluxes in inland waters. Here, we synthesize the most recent estimates of C fluxes and the differential contributions across inland waterbody types (rivers, streams, lakes, reservoirs, and ponds), including recent measurements that incorporate improved sampling methods, small waterbodies, and dried areas. Across all inland waters, we report a global C emission estimate of 4.40 Pg C/year (95% confidence interval: 3.95–4.85 Pg C/year), representing a 13% increase from the most recent estimate. We also review the mechanisms by which the most globally widespread anthropogenically driven climate and landscape changes influence inland water C fluxes. The majority of these drivers are expected to influence terrestrial C inputs to inland waters due to alterations in terrestrial C quality and quantity, hydrological pathways, and biogeochemical processing. We recommend four research priorities for the future study of anthropogenic alterations to inland water C fluxes: (1) before‐and‐after measurements of C fluxes associated with climate change events and landscape changes, (2) better quantification of C input from land, (3) improved assessment of spatial coverage and contributions of small inland waterbodies to C fluxes, and (4) integration of dried and drawdown areas to global C flux estimates. Improved measurements of inland water C fluxes and quantification of uncertainty in these estimates will be vital to understanding both terrestrial C losses and the "moving target" of inland water C emissions in response to rapid and complex anthropogenic pressures. [ABSTRACT FROM AUTHOR]

Linking biometric measurements of stand‐level biomass growth to tower‐based measurements of carbon uptake—gross primary productivity and net ecosystem productivity—has been the focus of numerous ecosystem‐level studies aimed to better understand the factors regulating carbon allocation to slow‐turnover wood biomass pools. However, few of these studies have investigated the importance of previous year uptake to growth. We tested the relationship between wood biomass increment (WBI) and different temporal periods of carbon uptake from the current and previous years to investigate the potential lagged allocation of fixed carbon to growth among six mature, temperate forests. We found WBI was strongly correlated to carbon uptake across space (i.e., long‐term averages at the different sites) but on annual timescales, WBI was much less related to carbon uptake, suggesting a temporal mismatch between C fixation and allocation to biomass. We detected lags in allocation of the previous year's carbon uptake to WBI at three of the six sites. Sites with higher annual WBI had overall stronger correlations to carbon uptake, with the strongest correlations to carbon uptake from the previous year. Only one site had WBI with strong positive relationships to current year uptake and not the previous year. Forests with low rates of WBI demonstrated weak correlations to carbon uptake from the previous year and stronger relationships to current year climate conditions. Our work shows an important, but not universal, role of lagged allocation of the previous year's carbon uptake to growth in temperate forests. Plain Language Summary: We compared the interannual variability of stand‐level biomass growth (estimated from annual tree‐level measurements) to carbon uptake (measured from towers monitoring gas exchange over forest canopies) to identify temporal mismatches between the two processes. We used data from multiple forested sites with long‐term measurements of carbon uptake to ask the question: is there a consistent temporal offset between the uptake of carbon and the allocation to plant biomass? We found that the relationship between growth and carbon uptake varies among sites, and there was no consistent temporal offset between the uptake of carbon and allocation to biomass growth. Sites with higher growth rates had higher interannual variability, and more apparent coupling between biomass growth and carbon uptake from the previous year. Forests with lower growth rates had weaker relationships with carbon uptake and stronger coupling with current year environmental conditions. We demonstrate that temporal lags between carbon uptake and allocation to growth are not universal among these temperate forests, and the carry‐over of uptake stored from the previous year is not as critical in slow‐growing forests, compared to fast‐growing forests, likely due to lower demands for growth. This work helps to clarify the limitations on the growth of slow‐turnover wood biomass. Key Points: We found evidence for lags in allocation of previous year's carbon uptake to wood biomass increment (WBI) at three of six sites studiedSites with high annual WBI demonstrated strong correlations to carbon uptake, with the strongest correlations including previous year uptakeWBI was less tightly coupled to carbon uptake in less productive forests that also exhibited low interannual variability in WBI [ABSTRACT FROM AUTHOR]

Simulations of the land surface carbon cycle typically compress functional diversity into a small set of plant functional types (PFT), with parameters defined by the average value of measurements of functional traits. In most earth system models, all wild plant life is represented by between five and 14 PFTs and a typical grid cell (≈100 × 100 km) may contain a single PFT. Model logic applied to this coarse representation of ecological functional diversity provides a reasonable proxy for the carbon cycle, but does not capture the non‐linear influence of functional traits on productivity. Here we show through simulations using the Energy Exascale Land Surface Model in 15 diverse terrestrial landscapes, that better accounting for functional diversity markedly alters predicted total carbon uptake. The shift in carbon uptake is as great as 30% and 10% in boreal and tropical regions, respectively, when compared to a single PFT parameterized with the trait means. The traits that best predict gross primary production vary based on vegetation phenology, which broadly determines where traits fall within the global distribution. Carbon uptake is more closely associated with specific leaf area for evergreen PFTs and the leaf carbon to nitrogen ratio in deciduous PFTs. Plain Language Summary: Plants play a critical role in the global carbon cycle, and diversity has been shown to influence vegetation productivity. However, when the land surface is simulated in a global model all wild plant life is reduced to a small number of plant functional types. Here we estimate how incorporating diversity influences ecosystem carbon uptake in 15 globe spanning landscapes. We find that diversity has a strong influence on modeled productivity, particularly in the arctic and tropics. Further, we find that whether plants shed their leaves annually has a strong influence on where traits fall within the global distribution and thus how traits and productivity interact. Key Points: We implemented distributions of leaf economic spectrum traits in a land surface model in 15 diverse landscapesTrait variation has a substantial influence on gross primary production (GPP)Phenology plays a key role in guiding where traits fall within the global trait distribution and hence trait‐GPP relationships [ABSTRACT FROM AUTHOR]

Environmental changes are anticipated to generate substantial impacts on carbon cycling in peatlands, affecting terrestrial‐climate feedbacks. Understanding how peatland methane (CH4) fluxes respond to these changing environments is critical for predicting the magnitude of feedbacks from peatlands to global climate change. To improve predictions of CH4 fluxes in response to changes such as elevated atmospheric CO2 concentrations and warming, it is essential for Earth system models to include increased realism to simulate CH4 processes in a more mechanistic way. To address this need, we incorporated a new microbial‐functional group‐based CH4 module into the Energy Exascale Earth System land model (ELM) and tested it with multiple observational data sets at an ombrotrophic peatland bog in northern Minnesota. The model is able to simulate observed land surface CH4 fluxes and fundamental mechanisms contributing to these throughout the soil profile. The model reproduced the observed vertical distributions of dissolved organic carbon and acetate concentrations. The seasonality of acetoclastic and hydrogenotrophic methanogenesis—two key processes for CH4 production—and CH4 concentration along the soil profile were accurately simulated. Meanwhile, the model estimated that plant‐mediated transport, diffusion, and ebullition contributed to ∼23.5%, 15.0%, and 61.5% of CH4 transport, respectively. A parameter sensitivity analysis showed that CH4 substrate and CH4 production were the most critical mechanisms regulating temporal patterns of surface CH4 fluxes both under ambient conditions and warming treatments. This knowledge will be used to improve Earth system model predictions of these high‐carbon ecosystems from plot to regional scales. Key Points: A new CH4 module was integrated into an Earth system model to predict CH4 fluxes in a northern Minnesota peatlandThe model accurately predicts the seasonal cycle of methane production and net fluxesCH4 substrate and production were the most critical mechanisms regulating temporal patterns of CH4 fluxes [ABSTRACT FROM AUTHOR]

Peatlands are one of the largest natural sources for atmospheric methane (CH4), a potent greenhouse gas. Climate warming and elevated atmospheric carbon dioxide (CO2) are two important environmental factors that have been confirmed to stimulate peatland CH4 emissions; however, the mechanisms underlying enhanced emissions remain elusive. A data‐model integration approach was applied to understand the CH4 processes in a northern temperate peatland under a gradient of warming and doubled atmospheric CO2 concentration. We found that warming and elevated CO2 stimulated CH4 emissions through different mechanisms. Warming initially stimulated but then suppressed vegetative productivity while stimulating soil organic matter (SOM) mineralization and dissolved organic carbon (DOC) fermentation, which led to higher acetate production and enhanced acetoclastic and hydrogenotrophic methanogenesis. Warming also enhanced surface CH4 emissions, which combined with warming‐caused decreases in CH4 solubility led to slightly lower dissolved CH4 concentrations through the soil profiles. Elevated CO2 enhanced ecosystem productivity and SOM mineralization, resulting in higher DOC and acetate concentrations. Higher DOC and acetate concentrations increased acetoclastic and hydrogenotrophic methanogenesis and led to higher dissolved CH4 concentrations and CH4 emissions. Both warming and elevated CO2 had minor impacts on CH4 oxidation. A meta‐analysis of warming and elevated CO2 impacts on carbon cycling in wetlands agreed well with a majority of the modeled mechanisms. This mechanistic understanding of the stimulating impacts of warming and elevated CO2 on peatland CH4 emissions enhances our predictability on the climate‐ecosystem feedback. Plain Language Summary: Peatlands are one of the largest natural sources for a potential greenhouse gas—methane. In this study, we took use of a number of field observational data to parameterize a microbial model before applying the model to understand the methane processes in a northern temperate peatland under a gradient of warming and doubled atmospheric carbon dioxide concentration. We found that warming and elevated carbon dioxide stimulated methane emissions through different mechanisms. Warming initially stimulated but then suppressed vegetative productivity while stimulating soil organic matter mineralization and dissolved organic carbon fermentation, which led to higher acetate production and enhanced methane production. Elevated carbon dioxide enhanced ecosystem productivity and soil organic carbon decomposition, resulting in higher dissolved organic carbon and acetate concentrations, which stimulate methane production. Both warming and elevated carbon dioxide had small impacts on methane oxidation. The modeling results are consistent with a global data synthesis. This mechanistic understanding of the stimulating impacts of warming and elevated carbon dioxide on peatland methane emissions enhances our ability to predict the interactions between the climate system and the terrestrial ecosystems. Key Points: Warming and elevated CO2 stimulate peatland CH4 emissions through different mechanismsThe stimulating impact of warming is primarily through stimulation of microbial processesThe stimulating impact of elevated CO2 is primarily through enhanced substrate availability by increased photosynthesis [ABSTRACT FROM AUTHOR]

Mechanistic photosynthesis models are at the heart of terrestrial biosphere models (TBMs) simulating the daily, monthly, annual and decadal rhythms of carbon assimilation (A). These models are founded on robust mathematical hypotheses that describe how A responds to changes in light and atmospheric CO2 concentration. Two predominant photosynthesis models are in common usage: Farquhar (FvCB) and Collatz (CBGB). However, a detailed quantitative comparison of these two models has never been undertaken. In this study, we unify the FvCB and CBGB models to a common parameter set and use novel multi‐hypothesis methods (that account for both hypothesis and parameter variability) for process‐level sensitivity analysis. These models represent three key biological processes: carboxylation, electron transport, triose phosphate use (TPU) and an additional model process: limiting‐rate selection. Each of the four processes comprises 1–3 alternative hypotheses giving 12 possible individual models with a total of 14 parameters. To broaden inference, TBM simulations were run and novel, high‐resolution photosynthesis measurements were made. We show that parameters associated with carboxylation are the most influential parameters but also reveal the surprising and marked dominance of the limiting‐rate selection process (accounting for 57% of the variation in A vs. 22% for carboxylation). The limiting‐rate selection assumption proposed by CBGB smooths the transition between limiting rates and always reduces A below the minimum of all potentially limiting rates, by up to 25%, effectively imposing a fourth limitation on A. Evaluation of the CBGB smoothing function in three TBMs demonstrated a reduction in global A by 4%–10%, equivalent to 50%–160% of current annual fossil fuel emissions. This analysis reveals a surprising and previously unquantified influence of a process that has been integral to many TBMs for decades, highlighting the value of multi‐hypothesis methods. [ABSTRACT FROM AUTHOR]

Tropical forests play a crucial role in the global carbon cycle, accounting for one third of the global net primary productivity and containing about 25% of global vegetation biomass and soil carbon. This is particularly true for tropical forests in the Amazon region, as these comprise approximately 50% of the world's tropical forests. It is therefore important for us to understand and represent the processes that determine the fluxes and storage of carbon in these forests. In this study, we show that the implementation of phosphorus (P) cycle and P limitation in the version 1 of the Energy Exascale Earth System Model land model (ELM v1) improves simulated spatial pattern of wood productivity. The P‐enabled ELM v1 is able to capture the declining west‐to‐east gradient of productivity, consistent with field observations. We also show that by improving the representation of mortality processes using soils data, ELMv1 is able to reproduce the observed spatial pattern of above ground biomass. Our model simulations show that the consideration of P availability leads to a smaller carbon sink associated with CO2 fertilization effect and lower carbon emissions due to land use and land cover change. Our simulations suggest P limitation would significantly reduce the carbon sink associated with CO2 fertilization effects through the 21st century. We conclude that P cycle dynamics affect both sources and sinks of carbon in the Amazon region, and the effects of P limitation would become increasingly important as CO2 increases. Therefore, P limitation must be considered for projecting future carbon dynamics in tropical ecosystems. Key Points: We implemented a prognostic phosphorus cycle and carbon‐nitrogen‐phosphorus interactions into ELM v1ELM v1 simulations show phosphorus cycle dynamics affect both sources and sinks of carbon in the Amazon regionThe effects of P limitation would become increasingly important as CO2 increases in the future [ABSTRACT FROM AUTHOR]

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2 and CH4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM‐Microbe, to examine the microtopographic impacts on CO2 and CH4 fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low‐centered polygon (LCP) center, LCP transition, LCP rim, high‐centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM‐Microbe model against static‐chamber measured CO2 and CH4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low‐elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH4 emissions rates with greater seasonal variations than high‐elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO2 + H2) is the most important factor determining CH4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area‐weighted approach before validation against EC‐measured CH4 fluxes. The model underestimated the EC‐measured CH4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4 flux. The strong microtopographic impacts on CO2 and CH4 fluxes call for a model‐data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape. Key Points: The CLM‐microbe model is able to simulate microtopographical impacts on CO2 and CH4 flux in the ArcticSubstrates availability for methanogenesis is the most important factor determining CH4 emissionThe strong microtopographic impacts call for a model‐data integration framework to better understand and predict C flux in the Arctic [ABSTRACT FROM AUTHOR]

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

Trend, detection, and attribution analyses were performed using naturalized streamflow observations and routed land surface model runoff for 10 subbasins in the Columbia River Basin during water years 1951–2008. The Energy Exascale Earth System land‐surface model (ELM) version 1.0 and the Routing Application for Parallel computatIon of Discharge (RAPID) routing model were used to conduct semi‐factorial simulations driven by multiple sets of bias‐corrected forcing data sets. Four main potential drivers, including climate change (CLMT), CO2 concentration (CO2), nitrogen deposition (NDEP), and land use and land cover change (LULCC), were analyzed during the assessment. All subbasins showed significant (α = 0.10) declines in the observed amount of annual total streamflow, except for the Middle and Upper Snake and Upper Columbia Subbasins. These declines were led by significant decreases in June–October streamflow, which also directly led to significant decreases in peak and summer streamflow. Except for the Snake River Subbasins, LULCC had the same pattern of declines in monthly streamflow, but the period was shifted to May–September. NDEP also had significant trends in June–October; however, rather than decreases, the trends showed significant increases in streamflow. While there were significant trends in CO2, NDEP, and LULCC, their signals of change were weak in comparison to the signal in CLMT and the natural internal variability found in streamflow. Overall, the detection and attribution analysis showed that the historical changes found in annual total, center of timing of, and summer mean streamflow could be attributed to changing climate and variability. Key Points: Latest NRNI data, ELM simulations, and river routing model were employed to study the long‐term hydrologic changes in CRBSignificant declines in the CRB June–October streamflow led to an earlier center of timing and significant decreases in annual total, summer mean, and peak streamflowThe changes in annual total, summer mean, and center of timing of streamflow were mainly attributed to changing climate and variability [ABSTRACT FROM AUTHOR]

Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems. Key Points: A GPP‐traceability framework is established to diagnose the uncertainty sources of modeled GPPLarge intermodel differences of modeled GPP result from their different representation of vegetation functional propertiesPositive bias in simulated GPP over the East Asian monsoon region could be attributed to the higher simulated CUE and SLA comparing with observations [ABSTRACT FROM AUTHOR]

Abstract: The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon‐flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux‐ versus pool‐based carbon cycle variables and (2) the time points when temperature and CO2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data‐model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux‐related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool‐related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast‐turnover pools to various CO2 and warming treatments were observed sooner than slow‐turnover pools. Our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future. [ABSTRACT FROM AUTHOR]

Water use efficiency (WUE), defined as the ratio of gross primary productivity and evapotranspiration at the ecosystem scale, is a critical variable linking the carbon and water cycles. Incorporating a dependency on vapor pressure deficit, apparent underlying WUE (uWUE) provides a better indicator of how terrestrial ecosystems respond to environmental changes than other WUE formulations. Here we used 20th century simulations from four terrestrial biosphere models to develop a novel variance decomposition method. With this method, we attributed variations in apparent uWUE to both the trend and interannual variation of environmental drivers. The secular increase in atmospheric CO2 explained a clear majority of total variation (66 ± 32%: mean ± one standard deviation), followed by positive trends in nitrogen deposition and climate, as well as a negative trend in land use change. In contrast, interannual variation was mostly driven by interannual climate variability. To analyze the mechanism of the CO2 effect, we partitioned the apparent uWUE into the transpiration ratio (transpiration over evapotranspiration) and potential uWUE. The relative increase in potential uWUE parallels that of CO2, but this direct CO2 effect was offset by 20 ± 4% by changes in ecosystem structure, that is, leaf area index for different vegetation types. However, the decrease in transpiration due to stomatal closure with rising CO2 was reduced by 84% by an increase in leaf area index, resulting in small changes in the transpiration ratio. CO2 concentration thus plays a dominant role in driving apparent uWUE variations over time, but its role differs for the two constituent components: potential uWUE and transpiration. [ABSTRACT FROM AUTHOR]

We are conducting a large-scale, long-term climate change response experiment in an ombrotrophic peat bog in Minnesota to evaluate the effects of warming and elevated CO2 on ecosystem processes using empirical and modeling approaches. To better frame future assessments of peatland responses to climate change, we characterized and compared spatial vs. temporal variation in measured C cycle processes and their environmental drivers. We also conducted a sensitivity analysis of a peatland C model to identify how variation in ecosystem parameters contributes to model prediction uncertainty. High spatial variability in C cycle processes resulted in the inability to determine if the bog was a C source or sink, as the 95% confidence interval ranged from a source of 50 g C m-2 yr-1 to a sink of 67 g C m-2 yr-1. Model sensitivity analysis also identified that spatial variation in tree and shrub photosynthesis, allocation characteristics, and maintenance respiration all contributed to large variations in the pretreatment estimates of net C balance. Variation in ecosystem processes can be more thoroughly characterized if more measurements are collected for parameters that are highly variable over space and time, and especially if those measurements encompass environmental gradients that may be driving the spatial and temporal variation (e.g., hummock vs. hollow microtopographies, and wet vs. dry years). Together, the coupled modeling and empirical approaches indicate that variability in C cycle processes and their drivers must be taken into account when interpreting the significance of experimental warming and elevated CO2 treatments. [ABSTRACT FROM AUTHOR]

Large uncertainty exists in model projections of the land carbon (C) sink response to increasing atmospheric CO2. Free-Air CO2 Enrichment (FACE) experiments lasting a decade or more have investigated ecosystem responses to a step change in atmospheric CO2 concentration. To interpret FACE results in the context of gradual increases in atmospheric CO2 over decades to centuries, we used a suite of seven models to simulate the Duke and Oak Ridge FACE experiments extended for 300 years of CO2 enrichment. We also determine key modeling assumptions that drive divergent projections of terrestrial C uptake and evaluate whether these assumptions can be constrained by experimental evidence. All models simulated increased terrestrial C pools resulting from CO2 enrichment, though there was substantial variability in quasi-equilibrium C sequestration and rates of change. In two of two models that assume that plant nitrogen (N) uptake is solely a function of soil N supply, the net primary production response to elevated CO2 became progressively N limited. In four of five models that assume that N uptake is a function of both soil N supply and plant N demand, elevated CO2 led to reduced ecosystem N losses and thus progressively relaxed nitrogen limitation. Many allocation assumptions resulted in increased wood allocation relative to leaves and roots which reduced the vegetation turnover rate and increased C sequestration. In addition, self-thinning assumptions had a substantial impact on C sequestration in two models. Accurate representation of N process dynamics (in particular N uptake), allocation, and forest self-thinning is key to minimizing uncertainty in projections of future C sequestration in response to elevated atmospheric CO2. [ABSTRACT FROM AUTHOR]

The strengths of future carbon dioxide (CO2) sinks are highly uncertain. A sound methodology to characterize current and predictive uncertainties in carbon cycle models is crucial for the design of efficient carbon management strategies. We demonstrate such a methodology, Markov Chain Monte Carlo (MCMC), by performing a Bayesian calibration of a simple global-scale carbon cycle model with historical carbon cycle observations to (1) estimate probability density functions (PDFs) of key carbon cycle parameters, (2) derive statistically sound probabilistic predictions of future CO2 sinks, and (3) assess the utility of hypothetical observation systems to reduce prediction uncertainties. We find that the PDFs of model parameter estimates are not normally distributed, and the residuals show statistically significant temporal autocorrelation. The assumption of normally distributed PDFs likely causes biased results, and the neglect of autocorrelation in the residual of the annual CO2 time series causes overconfidence in parameter estimates and predictions. Using interannually varying global temperature observations as forcing provides important information: terrestrial parameter PDFs are shifted and are more sharply constrained when compared to PDFs estimated when forcing the carbon cycle with a simple energy-balance model. Although CO2 observations provide a strong constraint on the total carbon sink, adding independent observations of terrestrial and oceanic fluxes has the potential to reduce uncertainty in predictions of this total sink more rapidly. Assimilating hypothetical annual observations of terrestrial and oceanic CO2 fluxes with realistic uncertainties reduces predictive uncertainties about CO2 sinks in the year 2050 by as much as a factor of 2 compared to assimilating CO2 concentrations alone. [ABSTRACT FROM AUTHOR]

A novel tree stem‐water model was developed to capture the dynamics of stem‐water storage and its contribution to daily transpiration. The module was incorporated into the Community Land Model (CLM), where it was used to test model sensitivity to stem‐water content for an evergreen rainforest site in Amazonia, that is, the BR‐Sa3 eddy covariance site. With the inclusion of the stem‐water storage, CLM produced greater dry‐season latent heat flux that was closer to observations, facilitated by easier canopy access to a nearby stem‐water source, rather than solely dependent on soil water. The simulated stem‐water content also showed seasonal variations in magnitude, along with the seasonal variations in sap flow rate. Stored stem‐water of a single mature tree was estimated to contribute 20–80 kg/day of water to transpiration during the wet season and 90–110 kg/day during the dry season, thereby partially replacing soil water and maintaining plant transpiration during the dry season. Diurnally, stem‐water content declined as water was extracted for transpiration in the morning and then was refilled from soil water beginning in the afternoon and through the night. The dynamic discharge and recharge of stem storage was also shown to be regulated by multiple environmental drivers. Our study indicates that the inclusion of stem capacitance in CLM significantly improves model simulations of dry‐season water and heat fluxes, in terms of both magnitude and timing. [ABSTRACT FROM AUTHOR]