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Abstract Central African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE‐AFP), which utilizes short‐wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE‐AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAIYoung) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE‐AFP successfully reproduces observed leaf turnover (R = 0.45) and young leaf abundance (R = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAIYoung and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE‐AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests.

Abstract Forests and grasslands play a significant role in global nitrous oxide (N2O) emission, while a sophisticated model is missing to interpret the microbial processes involved in N2O production and consumption in these ecosystems. A new N2O emission model, MicN model, was developed for natural forest and grassland ecosystems by integrating with Integrated BIosphere Simulator (IBIS). This new model includes four major N2O correlated processes: autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification and denitrifier denitrification. Thirty‐nine observation sites, including various forest and grassland ecosystem types, were selected to calibrate and validate IBIS‐MicN model. The IBIS‐MicN model explained 89% and 85% of observed daily variability in N2O emission for all calibration and validation sites, respectively. In addition, the model successfully predicted the concentrations of two important intermediates (i.e., ammonium and nitrate) for N2O production at nine validation sites. Compared with Forest‐DNDC model, IBIS‐MicN model performed better in simulating N2O emission, soil NH4+ and NO3−concentrations. At site scale, the simulated N2O emissions by IBIS‐MicN model positively correlated with annual mean soil temperature and annual precipitation, but negatively correlated with soil pH. This study provides an alternative model to estimate global N2O emission from natural forest and grassland ecosystems and to investigate corresponding environmental regulations.

Abstract Plant roots play a critical role in regulating the uptake of soil water and nutrients. Shifts in root growth and biomass distribution resulting from climate change can impact ecosystem water and carbon cycling. Such interactions between root growth and ecosystem functioning have not been integrated into current terrestrial ecosystem models. Here a three‐dimensional dynamic root model (DyRoot) was developed and implemented into the Integrated Biosphere Simulator (IBIS) ecosystem model for simulating root growth, architecture (i.e., fine and coarse roots), and distribution driven by soil water and nitrogen availabilities. Field root biomass observations were used for model calibration and validation. Results showed that DyRoot was able to simulate the root biomass distribution across five forest ecosystem types with good performance (R2 = 0.79, P < 0.01). The validations of root distribution in three forest chronosequences suggested that DyRoot captured the changes in rooting depth with the increase in stand age. The sensitivity of simulated root distribution to soil water availability was compared against root biomass observations from two precipitation reduction experiments. Results indicated that DyRoot reasonably represented the decrease of root biomass in topsoil layers in response to the deficits of soil moisture simulated by (IBIS). The DyRoot model algorithm in this study has great implications for representing an optimized root distribution in ecosystem models, which can improve model performance by simulating the feedbacks of root evolution in response to climate change. Furthermore, allowing explicit root architecture in the DyRoot model also sheds light on depicting the responses of root traits to environmental changes.

Abstract Wetlands are one of the most important terrestrial ecosystems for land‐atmosphere CH4 exchange. A new process‐based, biophysical model to quantify CH4 emissions from natural wetlands was developed and integrated into a terrestrial ecosystem model (Integrated Biosphere Simulator). The new model represents a multisubstance system (CH4, O2, CO2, and H2) and describes CH4 production, oxidation, and three transport processes (diffusion, plant‐mediated transport, and ebullition). The new model uses several critical microbial mechanisms to represent the interaction of anaerobic fermenters and homoacetogens, hydrogenotrophic, and acetoclastic methanogens, and methanotrophs in CH4 production and oxidation. We applied the model to 24 different wetlands globally to compare the simulated CH4 emissions to observations and conducted a sensitivity analysis. The results indicated that (1) for most sites, the model was able to capture the magnitude and variation of observed CH4 emissions under varying environmental conditions; (2) the parameters that regulate dissolved organic carbon and acetate production, and acetoclastic methanogenesis had the significant impact on simulated CH4 emissions; (3) the representation of the process components of CH4 cycling showed that CH4 oxidation was about half or more of CH4 production, and plant‐mediated transport was the dominant pathway at most sites; and (4) the seasonality of simulated CH4 emissions can be controlled by soil temperature, water table position, or combinations thereof.

Abstract Leaf phenology in the humid tropics largely regulates the seasonality of forest carbon and water exchange. However, it is inadequately represented in most global land surface models due to limited understanding of its controls. Based on intensive field studies at four Amazonian evergreen forests, we propose a novel, quantitative representation of tropical forest leaf phenology, which links multiple environmental variables with the seasonality of new leaf production and old leaf litterfall. The new phenology simulates higher rates of leaf turnover (new leaves replacing old leaves) in dry seasons with more sunlight, which is then implemented in ORCHIDEE, together with recent findings of ontogeny‐associated photosynthetic capacity, and is evaluated against ground‐based measurements of leaf phenology (canopy leaf area index and litterfall), eddy covariance fluxes (photosynthesis and latent heat), and carbon allocations from field observations. Results show the periodical cycles of solar radiation and vapor pressure deficit are the two most important environmental variables that are empirically related to new leaf production and old leaf abscission in tropical evergreen forests. The model with new representation of leaf phenology captures the seasonality of canopy photosynthesis at three out of four sites, as well as the seasonality of litterfall, latent heat, and light use efficiency of photosynthesis at all tested sites, and improves the seasonality of carbon allocations to leaves, roots, and sapwoods. This study advances understanding of the environmental controls on tropical leaf phenology and offers an improved modeling tool for gridded simulations of interannual CO2 and water fluxes in the tropics.