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AIM: To estimate the concentrations, stoichiometry and storage of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P) at biome and global scales. LOCATION: Global. METHOD: We collected 3422 data points to summarize the concentrations and stoichiometry of C, N and P in soils, soil microbial biomass at global and biome levels, and to estimate the global storage of soil microbial biomass C and N. RESULTS: The results show that concentrations of C, N and P in soils and soil microbial biomass vary substantially across biomes; the fractions of soil elements C, N and P in soil microbial biomass are 1.2, 2.6 and 8.0%, respectively. The best estimates of C:N:P stoichiometry for soil elements and soil microbial biomass are 287:17:1 and 42:6:1, respectively, at global scale, and they vary in a wide range among biomes. The vertical distribution of soil microbial biomass follows the distribution of roots up to 1 m depth. MAIN CONCLUSIONS: The global storage of soil microbial biomass C and N were estimated to be 16.7 Pg C and 2.6 Pg N in the 0–30 cm soil profiles, and 23.2 Pg C and 3.7 Pg N in the 0–100 cm soil profiles. We did not estimate P in soil microbial biomass due to insufficient data and insignificant correlation between soil total P and climate variables used for spatial extrapolation. The spatial patterns of soil microbial biomass C and N were consistent with those of soil organic C and total N, i.e. high density in northern high latitude, and low density in low latitudes and the Southern Hemisphere.

Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life-history traits and functions may be necessary to predict climate feedbacks owing to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here we developed the microbial enzyme-mediated decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, that is, MEND with dormancy (MEND) and MEND without dormancy (MEND_wod), against long-term (270 days) carbon decomposition data from laboratory incubations of four soils with isotopically labeled substrates. MEND_wod adequately fitted multiple observations (total C–CO 2 and 14 C–CO 2 respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. MEND improved estimates of microbial biomass by 20–71% over MEND_wod. We also quantified uncertainties in parameters and model simulations using the Critical Objective Function Index method, which is based on a global stochastic optimization algorithm, as well as model complexity and observational data availability. Together our model extrapolations of the incubation study show that long-term soil incubations with experimental data for multiple carbon pools are conducive to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field- and global-scale simulations, and enable more confident predictions of feedbacks between environmental change and carbon cycling.

Remote sensing can provide long-term and large-scale products helpful for ecosystem model evaluation. The authors compare monthly gross primary production (GPP) simulated by the Community Land Model, version 4 (CLM4) at a half-degree resolution with satellite estimates of GPP from the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (MOD17) for the 10-yr period January 2000–December 2009. The assessment is presented in terms of long-term mean carbon assimilation, seasonal mean distributions, amplitude and phase of the annual cycle, and intraannual and interannual GPP variability and their responses to climate variables. For the long-term annual and seasonal means, major GPP patterns are clearly demonstrated by both products. Compared to the MODIS product, CLM4 overestimates the magnitude of GPP for tropical evergreen forests. CLM4 has a longer carbon uptake period than MODIS for most plant functional types (PFTs) with an earlier onset of GPP in spring and a later decline of GPP in autumn. Empirical orthogonal function analysis of the monthly GPP changes indicates that, on the intraannual scale, both CLM4 and MODIS display similar spatial representations and temporal patterns for most terrestrial ecosystems except in northeast Russia and in the very dry region of central Australia. For 2000–09, CLM4 simulated increases in annual averaged GPP over both hemispheres; however, estimates from MODIS suggest a reduction in the Southern Hemisphere (−0.2173 PgC yr−1), balancing the significant increase over the Northern Hemisphere (0.2157 PgC yr−1). The evaluations highlight strengths and weaknesses of the CLM4 primary production and illuminate potential improvements and developments.

Soil organic matter (SOM) often increases when agricultural fields are converted to perennial vegetation, yet decadal scale rates and the mechanisms that underlie SOM accumulation are not clear. We measured SOM accumulation and changes in soil properties on a replicated chronosequence of former agricultural fields in the midwestern United States that spanned 40 years after perennial-grassland establishment. Over this time period, soil organic carbon (SOC) in the top 10 cm of soil accumulated at a constant rate of 62.0 g·m⁻²·yr⁻¹, regardless of whether the vegetation type was dominated by C₃ or C₄ grasses. At this rate, SOC contents will be equivalent to unplowed native prairie sites within 55-75 years after cultivation ceased. Both labile (short turnover time) and recalcitrant (long turnover time) carbon pools increased linearly for 40 years, with recalcitrant pools increasing more rapidly than expected. This result was consistent across several different methods of measuring labile SOC. A model that investigates the mechanisms of SOM formation suggests that rapid formation of stable carbon resulted from biochemically resistant microbial products and plant material. Former agricultural soils of the Great Plains may function as carbon sinks for less than a century, although much of the carbon stored is stable.

North America is both a source and sink of atmospheric carbon dioxide (CO 2 ). Continental sources - such as fossil-fuel combustion in the US and deforestation in Mexico - and sinks - including most ecosystems, and particularly secondary forests - add and remove CO 2 from the atmosphere, respectively. Photosynthesis converts CO 2 into carbon as biomass, which is stored in vegetation, soils, and wood products. However, ecosystem sinks compensate for only ~35% of the continent's fossil-fuel-based CO 2 emissions; North America therefore represents a net CO 2 source. Estimating the magnitude of ecosystem sinks, even though the calculation is confounded by uncertainty as a result of individual inventory- and model-based alternatives, has improved through the use of a combined approach.

We developed a microbial-enzyme-mediated decomposition (MEND) model, based on the Michaelis-Menten kinetics, that describes the dynamics of physically defined pools of soil organic matter (SOC). These include particulate, mineral-associated, dissolved organic matter (POC, MOC, and DOC, respectively), microbial biomass, and associated exoenzymes. The ranges and/or distributions of parameters were determined by both analytical steady-state and dynamic analyses with SOC data from the literature. We used an improved multi-objective parameter sensitivity analysis (MOPSA) to identify the most important parameters for the full model: maintenance of microbial biomass, turnover and synthesis of enzymes, and carbon use efficiency (CUE). The model predicted that an increase of 2°C (baseline temperature 12°C) caused the pools of POC-cellulose, MOC, and total SOC to increase with dynamic CUE and decrease with constant CUE, as indicated by the 50% confidence intervals. Regardless of dynamic or constant CUE, the changes in pool size of POC, MOC, and total SOC varied from −8% to 8% under +2°C. The scenario analysis using a single parameter set indicates that higher temperature with dynamic CUE might result in greater net increases in both POC-cellulose and MOC pools. Different dynamics of various SOC pools reflected the catalytic functions of specific enzymes targeting specific substrates and the interactions between microbes, enzymes, and SOC. With the feasible parameter values estimated in this study, models incorporating fundamental principles of microbial-enzyme dynamics can lead to simulation results qualitatively different from traditional models with fast/slow/passive pools.

Soil respiration is an important component of the global carbon cycle and is highly responsive to changes in soil temperature and moisture. Accurate prediction of soil respiration and its changes under future climatic conditions requires a clear understanding of the processes involved. Most current empirical soil respiration models incorporate just few of the underlying mechanisms that may influence its response. In this study, a new partially process-based component model that separately treated several source components of soil respiration was tested with data from a climate change experiment that manipulated atmospheric [CO 2 ], air temperature and soil moisture. Results from this model were compared to results from other widely used models with the parameters fitted using experimental data. Using the component model, we were able to estimate the relative proportions of heterotrophic and autotrophic respiration in total soil respiration for each of the different treatments. The value of the Q 10 parameters for temperature response component of all of the models showed sensitivity to soil moisture. Estimated Q 10 parameters were higher for wet treatments and lower for dry treatments compared to the values estimated using either the data from all treatments or from only the control treatments. Our results suggest that process-based models provide a better understanding of soil respiration dynamics under changing environmental conditions, but the extent and contribution of different source components need to be included in mechanistic and process-based soil respiration models at corresponding scales.

Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land-atmosphere CO2 fluxes, and have great potential to predict the terrestrial ecosystem response to changing climate. The skill of models that provide continental scale carbon flux estimates, however, remains largely untested. This paper evaluates the performance of continental-scale flux estimates from 17 models against observations from 36 North American flux towers. Fluxes extracted from regional model simulations are compared with co-located flux tower observations at monthly and annual time increments. Site-level model simulations are used to help interpret sources of the mismatch between the regional simulations and site-based observations. On average the regional model runs overestimate the annual gross primary productivity (5%) and total respiration (15%), and significantly underestimate the annual net carbon uptake (64%) during the time period 2000-2005. Comparison with site-level simulations implicate choices specific to regional model simulations as contributors to the gross flux biases, but not the net carbon uptake bias. The models perform the best at simulating carbon exchange at deciduous broadleaf sites; likely because a number of models use prescribed phenology to simulate seasonal fluxes. The models do not perform as well for crop, grass and evergreen sites. The regional models match the observations most closely in terms of seasonal correlation and seasonal magnitude of variation, but have very little skill at inter-annual correlation and minimal skill at inter-annual magnitude of variability. The comparison of site versus regional level model runs demonstrate that 1) the inter-annual correlation is higher for site-level model runs but the skill remains low, and 2) the underestimation of year-to-year variability for all fluxes is an inherent weakness of the models. The best performing regional models that do not use flux tower calibration are CLM-CN, CASA-GFEDv2 and SIB3. Two flux tower calibrated, empirical models, EC-MOD and MOD17+, perform as well as the best process-based models. This suggests that 1) empirical, calibrated models can perform as well as complex, process-based models, and 2) combining process-based model structure with relevant constraining data could significantly improve model performance.

This paper examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset, derived from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data set, multiple other remote sensing datasets, and site level observations, we evaluated the CLM4 FPAR’s seasonal cycle, diurnal cycle, long-term trends, and spatial patterns. Our findings show that the model generally agrees with observations in the seasonal cycle, long-term trends, and spatial patterns, but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months, and spatial heterogeneity. We identify the discrepancy in the diurnal cycle as, due to, the absence of dependence on sun angle in the model. Implementation of sun angle dependence in a one-dimensional (1-D) model is proposed. The need for better relating of vegetation to climate in the model, indicated by long-term trends, is also noted. Evaluation of the CLM4 land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets provides targets for future development in its representation of this naturally complicated process.

Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model‐data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties are then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model‐data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model‐data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error. Key Points Measurements of carbon pools at flux towers are important model constraints Longer data records reduce model parameter and prediction uncertainty Model parameters associated with carbon pool turnover are poorly constrained