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Urban afforestation is considered a promising nature-climate solution that may contribute to achieve climate neutrality by 2050, since it can increase C-storage and C-sequestration, whilst providing further multiple ecosystem services for citizens. However, the quantification of the CO 2 sequestration capacity that may be provided by an urban forest as well as the capacity to impact the city-level C-balance and offset anthropogenic emissions is a complex issue. Methodological approaches, quantity and quality of information contained in urban tree database, and the level of detail of the planned urban forest can strongly influence the estimation of C-sequestration potential offered by urban forests. In this work, an integrated framework based on emission inventory, tree species/morphology and ecosystem modelling has been proposed for the city of Prato, Italy, a representative medium size European city to: i) evaluate the current C-sequestration capacity of urban trees; ii) upscale such capacity with different afforestation scenarios, iii) compare the sink capacity offered by ecosystems with current and projected anthropogenic emissions. Results indicated that the green areas within the Municipality of Prato can sequester 33.1 ktCO 2 yr -1 under actual conditions and 51.0 ktCO 2 yr -1 under the afforestation scenario which maximize the CO 2 sequestration capacity, offsetting the 7.1 % and 11 % of the total emissions (465.8 ktCO 2 yr -1 ), respectively. This study proves that, in the various afforestation scenarios tested, the contribution of urban afforestation to the municipality carbon balance is negligible and that carbon neutrality can only be reached by the substantial decarbonization of emission sectors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models., (© 2020 John Wiley & Sons Ltd.)

Optical and LiDAR datasets taken by airborne platforms are informative on important forest attributes, and particularly on growing stock volume (GSV), which is related to woody biomass and carbon storage. This paper presents the integration of such datasets with information collected on the ground for assessing the GSV variation that occurred in the Cascine urban park (Florence, Italy) mostly as a consequence of an extreme wind storm in March 2015. Two LiDAR acquisitions taken before and after the disastrous event (2007 and 2017) were combined with conventional forest observations derived from a complete inventory of the park area and from restricted ground surveys conducted around the first and second study dates. The dataset of the first period was used to evaluate the applicability of an area-based estimation approach to the second dataset and consequently assess the magnitude of the GSV change. In particular, the change was statistically characterized based only on the recently collected ground samples and on the combination of these with LiDAR data through a ratio estimator. The combination of the LiDAR and ground data increased the precision of the estimates obtained, highlighting a significant GSV decrease during the study decade (about 20%), that was concentrated in two of four park sectors. This GSV decrease, which can be mostly attributed to the effect of the 2015 wind storm, corresponds to a total loss of carbon stored in the park of around 2700 tons. [ABSTRACT FROM AUTHOR]