No abstract

Abstract. Dynamic soil models are needed for estimating impact of weather and climate change on soil carbon stocks and fluxes. Here, we evaluate performance of Yasso07 and ROMULv models against forest soil carbon stock measurements. More specifically, we ask if litter quantity, litter quality and weather data are sufficient drivers for soil carbon stock estimation. We also test whether inclusion of soil water holding capacity improves reliability of modelled soil carbon stock estimates. Litter input of trees was estimated from stem volume maps provided by the National Forest Inventory, while understorey vegetation was estimated using new biomass models. The litter production rates of trees were based on earlier research, while for understorey biomass they were estimated from measured data. We applied Yasso07 and ROMULv models across Finland and ran those models into steady state; thereafter, measured soil carbon stocks were compared with model estimates. We found that the role of understorey litter input was underestimated when the Yasso07 model was parameterised, especially in northern Finland. We also found that the inclusion of soil water holding capacity in the ROMULv model improved predictions, especially in southern Finland. Our simulations and measurements show that models using only litter quality, litter quantity and weather data underestimate soil carbon stock in southern Finland, and this underestimation is due to omission of the impact of droughts to the decomposition of organic layers. Our results also imply that the ecosystem modelling community and greenhouse gas inventories should improve understorey litter estimation in the northern latitudes.

We can curb climate change by improved management decisions for the most important terrestrial carbon pool, soil organic carbon stock (SOC). However, we need to be confident we can obtain the correct representation of the simultanous effect of the input of plant litter, soil temperature and water (which could be altered by climate or management) on the decomposition of soil organic matter. In this research, we used regression and Bayesian statistics for testing process‐based models (Yasso07, Yasso15 and CENTURY) with soil heterotrophic respiration (Rh) and SOC, measured at four sites in Finland during 2015 and 2016. We extracted climate modifiers for calibration with Rh. The Rh values of Yasso07, Yasso15 and CENTURY models estimated with default parameterization correlated with measured monthly heterotrophic respiration. Despite a significant correlation, models on average underestimated measured soil respiration by 43%. After the Bayesian calibration, the fitted climate modifier of the Yasso07 model outperformed the Yasso15 and CENTURY models. The Yasso07 model had smaller residual mean square errors and temperature and water functions with fewer, thus more efficient, parameters than the other models. After calibration, there was a small overestimate of Rh by the models that used monotonic moisture functions and a small generic underestimate in autumn. The mismatch between measured and modelled Rh indicates that the Yasso and CENTURY models should be improved by adjusting climate modifiers of decomposition or by accounting for missing controls in, for example, microbial growth. Highlights We tested soil carbon models against monthly soil Rh fluxes and amounts of SOC stock. The models accurately reproduced most of the seasonal Rh trends and amounts of SOC. Under autumn temperature and moisture, Rh was mismatched before and even after the parameterization. The seasonality of the temperature and water functions should be adjusted in models.

<p><strong>Abstract.</strong> Inaccurate estimate of the largest terrestrial carbon pool, soil organic carbon (SOC) stock, is the major source of uncertainty in simulating feedback of climate warming on ecosystematmosphere carbon exchange by process based ecosystem and soil carbon models. Although the models need to simplify complex environmental processes of soil carbon sequestration, in a large mosaic of environments a missing key driver could lead into a modelling bias in predictions of SOC stock change. <br><br> We aimed to evaluate SOC stock estimates of process based models (Yasso07, Q, and CENTURY) against the Swedish forest soil inventory data (3230 samples) organized by recursive partitioning method into distinct soil groups with underlying SOC stock development linked to physicochemical conditions. <br><br> The Yasso07 and Q models that used only climate and litterfall input data and ignored soil properties generally agreed with two third of measurements. However, in fertile sites with high nitrogen deposition, high cation exchange capacity, or moderately increased soil water content, Yasso07 and Q underestimated SOC stocks. Accounting for soil texture (clay, silt, and sand content) and structure (bulk density) in CENTURY model showed no improvement on carbon stock estimates, as CENTURY deviated in similar manner. <br><br> Our analysis suggested that the soils with poorly predicted SOC stocks, as characterized by the high nutrient status and well sorted parent material, indeed have had other predominat drivers of SOC stabilization lacking in the models presumably the mycorrhizal organic uptake and organo-mineral stabilization processes. Our results imply that the role of soil nutrient status as regulator of organic matter mineralization has to be re-evaluated, since correct steady state SOC stocks are decisive for predicting future SOC change.</p>

Abstract. Future climate change will dramatically change the carbon balance in the soil, and this change will affect the terrestrial carbon stock and the climate itself. Earth system models (ESMs) are used to understand the current climate and to project future climate conditions, but the soil organic carbon (SOC) stock simulated by ESMs and those of observational databases are not well correlated when the two are compared at fine grid scales. However, the specific key processes and factors, as well as the relationships among these factors that govern the SOC stock, remain unclear; the inclusion of such missing information would improve the agreement between modeled and observational data. In this study, we sought to identify the influential factors that govern global SOC distribution in observational databases, as well as those simulated by ESMs. We used a data-mining (machine-learning) (boosted regression trees -BRT) scheme to identify the factors affecting the SOC stock. We applied BRT scheme to three observational databases and 15 ESM outputs from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and examined the effects of 13 variables/factors categorized into five groups (climate, soil property, topography, vegetation, and land-use history). Globally, the contributions of mean annual temperature, clay content, carbon-to-nitrogen (CN) ratio, wetland ratio, and land cover were high in observational databases, whereas the contributions of the mean annual temperature, land cover, and net primary productivity (NPP) were predominant in the SOC distribution in ESMs. A comparison of the influential factors at a global scale revealed that the most distinct differences between the SOCs from the observational databases and ESMs were the low clay content and CN ratio contributions, and the high NPP contribution in the ESMs. The results of this study will aid in identifying the causes of the current mismatches between observational SOC databases and ESM outputs and improve the modeling of terrestrial carbon dynamics in ESMs. This study also reveals how a data-mining algorithm can be used to assess model outputs.

The pipe model approach was compared with foliage biomass models by using the cross-sectional area at the tree crown base for predicting foliage biomass of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). We evaluated the impacts of site type, fertilization, and climate on the relationship between foliage biomass and cross-sectional area at the tree crown base, referred as to the pipe model ratio. Our hypotheses were that (i) the pipe model approach is a more precise and accurate method for foliage prediction than the traditional biomass models and (ii) the pipe model ratio for foliage does not explicitly depend on any single environmental driver. Data used here consisted of felled trees from Finnish and Swedish biomass studies. These data were analyzed by linear mixed models with different covariates, and the uncertainties of different modelling approaches were evaluated. The pipe model outperformed other models for Scots pine but not for Norway spruce. Results showed larger pipe model ratios for Scots pine in herb-rich forests compared with those of trees in subxeric heath forest. Results from fertilized trees indicated that the addition of nitrogen temporarily increased foliage biomass.

No abstract

No abstract