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We integrate a carbon net sink and stand-level wood production to analyze their simultaneous optimization as an economic problem. Carbon is included in living trees, wood products, and forest soil. Forestry is specified by a size-structured model for optimizing thinning timing and intensity, rotation period, and the optimal choice of rotation versus continuous cover forestry. The optimal inclusion of a carbon net sink increases the carbon pool mainly in living trees and forest soil, while the effect on the product carbon pool remains minor. With a 3% interest rate, increasing the C[O.sub.2] price to 40 [euro] per tC[O.sub.2] increases the total steady-state carbon pool by 131% and the soil carbon accounts for ca. 60% of the increased carbon storage. Omitting soil carbon, as in previous studies, leads to underestimates of the carbon sink, significantly decreasing the optimal total C[O.sub.2] net sink and achievable economic net gain from joint wood production and carbon management. The inclusion of soil carbon suggests that, in contrast to previous results, a higher C[O.sub.2] price does not necessarily favor continuous cover forestry. Key words: carbon sequestration, economic profitability, soil carbon, continuous cover forestry, rotation forestry, optimal rotation, 1. Introduction Climate change and the aim of limiting global warming have strongly increased the attention given to forests as carbon sinks (Shukla et al. 2019). Compared with the tropics, [...]

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real-world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first-generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.

Soil carbon sequestration has gained traction as a mean to mitigate rising atmospheric carbon dioxide concentrations. Verification of different methods' efficiency to increase soil carbon sink requires, in addition to good quality measurements, reliable models capable of simulating the effect of the sequestration practises. One way to get insight of the methods' effects on carbon cycling processes is to analyse different carbon isotope concentrations in soil organic matter. In this paper we introduce a carbon-13 isotope specific soil organic matter decomposition add-on into the Yasso soil carbon model and assess its functionality. The new 13C-dedicated decomposition is straightforward to implement and depends linearly on the default Yasso model parameters and the relative carbon isotope (13C/12C) concentration. Despite of their simplicity, the modifications considerably improve the model behaviour in a 50-year long simulation. [ABSTRACT FROM AUTHOR]

Ecosystems dominated by plants featuring ectomycorrhizae (EM) and arbuscular mycorrhizae (AM) promote distinct soil carbon dynamics. AM and EM soil environments can thus have different impacts on litter decomposition. However, current soil carbon models treat mycorrhizal impacts on the processes of soil carbon transformation as a black box. We re-formulated the soil carbon model Yasso15, and incorporated impacts of mycorrhizal vegetation on soil carbon pools of different recalcitrance. We examined alternative conceptualizations of mycorrhizal impacts on transformations of labile and stable carbon, and quantitatively assessed the performance of the selected optimal model in terms of the long-term fate of plant litter. We found that mycorrhizal impacts on pools of labile carbon in the litter are distinct from that on recalcitrant pools. Plant litter of the same chemical composition decomposes slower when exposed to EM-dominated ecosystems compared to AM-dominated ones, and across time, EM-dominated ecosystems accumulate more recalcitrant residues of non-decomposed litter. Overall, adding our mycorrhizal module into the Yasso model improved the accuracy of the temporal dynamics of carbon sequestration predictions. Our results suggest that mycorrhizal impacts on litter decomposition are underpinned by distinct decomposition pathways in AM- and EM-dominated ecosystems. Ignoring mycorrhiza-induced mechanisms will thus lead to an overestimation of climate impacts on decomposition dynamics. Our new model provides a benchmark for mechanistic and quantitative modelling of microbial impact on soil carbon. It helps to determine the relative importance of mycorrhizal associations and climate on organic matter decomposition rate and reduces the uncertainties in estimating soil carbon sequestration. [ABSTRACT FROM AUTHOR]

Soil Organic Carbon (SOC) models are important tools in determining global SOC distributions and how carbon stocks are affected by climate change. Their performances are, however, affected by data and methods used to calibrate them. Here we study how the Yasso SOC model performs if calibrated individually or with multiple datasets and how the chosen calibration method affected the parameter estimation. We found that when calibrated with multiple datasets, the model showed a better global performance compared to a single dataset calibration. Furthermore, our results show that more advanced calibration algorithms should be used for SOC models due to the multiple local maximas in the likelihood space. [ABSTRACT FROM AUTHOR]

Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support. [ABSTRACT FROM AUTHOR]

Ecosystem models are often calibrated and/or validated against derived remote sensing data products, such as MODIS leaf area index. However, these data products are generally based on their own models, whose assumptions may not be compatible with those of the ecosystem model in question, and whose uncertainties are usually not well quantified. Here, we develop an alternative approach whereby we modify an ecosystem model to predict full-range, high spectral resolution surface reflectance, which can then be compared directly against airborne and satellite data. Specifically, we coupled the two-stream representation of canopy radiative transfer in the Ecosystem Demography model (ED2) with a leaf radiative transfer model (PROSPECT 5) and a simple soil reflectance model. We then calibrated this model against reflectance observations from the NASA Airborne VIsible/InfraRed Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration successfully constrained the posterior distributions of model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. The calibrated model was able to accurately reproduce surface reflectance and leaf area index for sites with highly varied forest composition and structure, using a single common set of parameters across all sites. We conclude that having dynamic vegetation models directly predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data. [ABSTRACT FROM AUTHOR]

Model-calculated forecasts of soil organic carbon (SOC) are important for approximating global terrestrial carbon pools and assessing their change. However, the lack of detailed observations limits the reliability and applicability of these SOC projections. Here, we studied if state data assimilation (SDA) can be used to continuously update the modeled state with available total carbon measurements in order to improve future SOC estimations. We chose six fallow test sites with measurements time series spanning 30 to 80 years for this initial test. In all cases, SDA improved future projections but to varying degrees. Furthermore, already including the first few measurements impacted the state enough to reduce the error in decades long projections in by at least 1 t C ha−1. Our results show the benefits of implementing SDA methods for forecasting SOC, but also highlight implementation aspects that need consideration and further research. [ABSTRACT FROM AUTHOR]

We calibrated the JSBACH model with six different stomatal conductance formulations using measurements from 10 FLUXNET coniferous evergreen sites in the Boreal zone. The parameter posterior distributions were generated by adaptive population importance sampler and the optimal values by a simple stochastic optimisation algorithm. The observations used to constrain the model are evapotranspiration (ET) and gross primary production (GPP). We identified the key parameters in the calibration process. These parameters control the soil moisture stress function and the overall rate of carbon fixation. We were able to improve the coefficient of determination and the model bias with all stomatal conductance formulations. There was no clear candidate for the best stomatal conductance model, although certain versions produced better estimates depending on the examined variable (ET, GPP) and the used metric. We were also able to significantly enhance the model behaviour during a drought event in a Finnish Scots pine forest site. The JSBACH model was also modified to use a delayed effect of temperature for photosynthetic activity. This modification enabled the model to correctly time and replicate the springtime increase in GPP (and ET) for conifers throughout the measurements sites used in this study. [ABSTRACT FROM AUTHOR]