Among the many natural disturbances that affect Canada's boreal and temperate forest biomes, wildfire has the greatest impact on forest productivity, landscape structure, timber supply and greenhouse gas emissions. Fire severity represents the fate of biomass, by partitioning survival, consumption, and mortality without consumption. Currently, the impact of fire on carbon stocks in Canada's forest carbon accounting is limited to a single parametrization of fire severity that assumes only high severity fires, despite evidence of widespread mixed-severity fires. This paper describes a sub-model, termed FireDMs (Fire Disturbance Matrix: severity), of the National Forest Carbon Monitoring Accounting and Reporting System for Canada (NFCMARS). In this sub-model, field measurements of biomass consumption are related to satellite-derived burn severity maps and are interpreted from a fire physics and ecology perspective to derive estimates of the forest greenhouse gas emissions in the immediate aftermath of fires. The sub-model also quantifies fire-killed but uncombusted biomass as a set of distinct pools. Model outputs indicate total direct carbon emissions range from a 11 t C ha-1 in Boreal Shield West forests of Saskatchewan following low severity fire to over 60 t C ha-1 in Pacific Maritime forests of British Columbia under high severity fire. The existing approach to emissions in NFCMARS yields regional CO2-equivalent emissions that are typically 10 % to 25 % higher than this new method, owing to lower overall canopy consumption with mixed-severity fires, which is only partially offset by increased estimates of forest floor consumption in this new approach. Comparisons against directly observed fire plume emissions ratios as well as against annualized carbon emissions for Canada's 2023 fire season show good model agreement with observations.

Canada’s boreal zone covers almost 60

AbstractThe Hudson Bay Lowlands (HBL) are recognized as the second largest peatland complex in the world. Due to variability in peat thickness across a large and heterogeneous landscape, the existing carbon (C) storage estimates for the HBL may contain large uncertainty. Here, we use geospatial variables that are associated with HBL peat formation, age, accumulation, and occurrence to understand the driving factors for peat depth variability and map peat depth and C storage at 30 m spatial resolution. The estimated average peat depth of HBL is 184(±48) cm with 90% of values falling between 89 and 264 cm. Based on the spatially explicit peat depth, the HBL total C storage is estimated to be 30(±6) Pg. Distance to the coastline is the most important indicator of peat depth where the depth increases with distance further away from Hudson Bay coastline, confirming that the time since peat formation is closely related to peat depth.

Mountain pine beetles have caused extensive tree mortality in Canadian boreal forests. In some areas, ectomycorrhizal (EM) pine stands have been replaced by arbuscular mycorrhizal (AM) vegetation. Given the distinct effects EM and AM fungi have on nutrient and carbon cycling, this study examined whether shifts in mycorrhizal dominance post-disturbance would coincide with changes in soil carbon. We predicted reduced total mineral soil carbon stocks in disturbed stands but increased carbon allocation into stable mineral-associated organic matter (MAOM). We surveyed soils and vegetation in 80 lodgepole pine-dominated sites; half disturbed 12–16 years prior and half intact. We separated soils into MAOM and particulate organic matter (POM) to assess the size and carbon and nitrogen concentrations of each fraction. Despite distinct mycorrhizal group dominance between disturbances, disturbed forests held similar total mineral soil carbon stocks as intact sites, while C/N ratios were lower. In coarse-textured soils, disturbed sites had lower C/N in POM compared to intact sites. No differences in MAOM C/N were detected between disturbances, and the proportion of carbon stored in MAOM versus POM remained unchanged. The lower C/N of mineral soil in disturbed sites likely reflects higher-quality litters from AM vegetation. Still, the influence of mycorrhizal type on soil carbon should be interpreted cautiously, as variations in litter quality among AM species and abiotic factors could confound results. This study suggests that soil carbon stores in disturbed boreal forests are resistant, and mycorrhizal shifts alone may not substantially alter long-term soil carbon dynamics.

Persistent discrepancies between bottom‐up, terrestrial biosphere models (TBMs), and top‐down, atmospheric inversions, have made it difficult to quantify the magnitude of the North American terrestrial carbon sink. Previous studies have compared aggregated continent‐scale estimates of carbon fluxes from TBMs and inversions for all of North America, but this provides limited insights into finer‐scale mismatches that contribute to the overall discrepancies. Here we evaluate agreement between TBM and inversion carbon flux estimates at 1° × 1° resolution to provide more direct insights into where models disagree and what underlying factors drive discrepancies. We find that the additional carbon uptake estimated by inversions, in just 16% of the area of North America, is large enough to account for the discrepancy between TBMs and inversions across the whole continent. The majority of these differences occur in permafrost, peatland, and cropland regions. In these regions, we find a higher likelihood of potential biases in the weaker sink estimates from TBMs, suggesting that the stronger sink implied by inversions is more likely to be realistic. However, the current observational coverage is insufficient for fully assessing the causes of discrepancies or the magnitude of biases in either approach. Encouragingly, improved representation of agricultural processes in a TBM led to better agreement with inversions in croplands. Efforts to accurately model cropland dynamics will help improve agreement between TBMs and inversions. Overall, this work presents a clear path for reconciling the discrepancies between inversion and TBM estimates of the North American carbon sink that have persisted for two decades.

This study applied the Canadian Model for Peatlands (CaMP) to 63.9 million hectares of peatlands within boreal and temperate ecozones of Canada to assess the trends in atmospheric carbon (C) emissions and removals and C sequestration over 30 years (1990–2019). The CaMP modelled net ecosystem productivity (NEP) for peatlands within the study area indicated a net C sink at an annual mean rate of 30.9 Mt C y−1 (48.4 g C m−2 y−1). Net Biome Productivity (NBP), which accounts for losses of carbon due to wildfire, reduced the C sink to 19.0 Mt C y−1 (29.8 g C m−2 y−1). On an area-weighted basis, the Hudson Plains and the Boreal Plains had the highest NBP (34.9 and 34.0 g C m−2 y−1, respectively) and the Atlantic Maritime and Boreal Shield West had the lowest (25.3 and 24.6 g C m−2 y−1 respectively), with the Boreal Shield East having intermediate NBP (27.5 g C m−2 y−1). NBP was highest in peatlands with forest cover, rising with increasing nutrient status (bog < poor fen < rich fen). These modelled values compare well with long-term carbon accumulation rates found in the literature for Canadian peatlands ranging from 6 to 70 g C m−2 y−1. While most years peatlands were a net sink of C, years with extensive fires resulted in peatlands being a small net source of C. The study highlighted that forested peatlands were important in driving the C sequestration sink but were also sensitive to climate warming due to high rates of soil CO2 emission and large wildfire C emissions. This highlights an important, yet vulnerable role these forested peatlands play in Canada's national greenhouse gas accounting. While this research is the first to produce estimates of C sequestration and greenhouse gas emission and removal rates across such a large area of Canada, further research is required across peatland types and ecozones to improve parameterization, validation, and process representations. Our results stress the importance of ecozone-specific analyses and accounting for infrequent large fire years and fire risk in land management policy and carbon accounting.

Peatlands cover approximately 12% of the Canadian landscape and play an important role in the carbon cycle through their centennial- to millennial-scale storage of carbon under waterlogged and anoxic conditions. In recognizing the potential of these ecosystems as natural climate solutions and therefore the need to include them in national greenhouse gas inventories, the Canadian Model for Peatlands module (CaMP v. 2.0) was developed by the Canadian Forest Service. Model parameterization included compiling peat profiles across Canada to calibrate peat decomposition rates from different peatland types, to define typical bulk density profiles, and to describe the hydrological (i.e., water table) response of peatlands to climatic changes. A total of 1217 sites were included in the dataset from published and unpublished sources. The CORESITES table contains site location and summary data for each profile, as well as an estimate of total carbon mass per unit area (in megagrams of C per hectare). Total carbon mass per unit area at each location was calculated using bulk density and carbon content through each profile. The PROFILES table contains data for depth (in centimeters), bulk density (in grams per cubic meter), ash and carbon content (in percentage), and material descriptions for contiguous samples through each peat profile. Data gaps for bulk density and C content were filled using interpolation, regression trees, and assigned values based on material description and/or soil classification to allow for the estimation of total carbon mass per unit area. A subset of the sites (N = 374) also have pH and pore water trace-elemental geochemistry data and are found in the WATER table. The REFERENCES table contains the full citation of each source of the data and is linked to each core location through the SOURCEDATA table. The LOOKUP table defines codes in the database that required more space that what was sufficient in the metadata tables. The data can be accessed on Open Government Canada and will be useful for future work on carbon stock mapping and ecosystem modeling. All metadata and data are provided © Her Majesty the Queen in Right of Canada, 2023 and information contained in this publication may be reproduced for personal or public noncommercial purposes with attribution, whereas commercial reproduction and distribution are prohibited except with written permission from NRCan; complete details are noted in the Supporting Information file Metadata S1 (see Class III.B.3: Copyright restrictions).

Epidemic Models of the Onset of Social Activities (EMOSA) describe behaviors that spread through social networks. Two social influence methods are represented, social contagion (one-to-one spread) and general diffusion (spread through cultural channels). Past models explain problem behaviors-smoking, drinking, sexuality, and delinquency. We provide review, and a tutorial (including examples). Following, we present new EMOSA models explaining changes in adolescent and young adult religious participation. We fit the model to 10 years of data from the 1997 U.S. National Longitudinal Survey of Youth. Innovations include a three-stage bi-directional model, Bayesian Markov Chain Monte Carlo (MCMC) estimation, graphical innovations, and empirical validation. General diffusion dominated rapid reduction in church attendance during adolescence; both diffusion and social contagion explained church attendance stability in early adulthood.

Soil is the largest terrestrial carbon (C) reservoir and a large potential source or sink of atmospheric CO ₂ . Soil C models have usually focused on refining representations of microbe‐mediated C turnover, whereas lateral hydrologic C fluxes have largely been ignored at regional and global scales. Here, we provide large‐scale estimates of hydrologic export of soil organic carbon (SOC) and its effects on bulk soil C turnover rates. Hydrologic export of SOC ranged from nearly 0 to 12 g C m −2 yr −1 amongst catchments across the conterminous United States, and total export across this region was 14 (95% CI 4‐41) Tg C/yr. The proportion of soil C turnover attributed to hydrologic export ranged from <1% to 20%, and averaged 0.97% (weighted by catchment area; 95% CI 0.3%–2.6%), with the lowest values in arid catchments. Ignoring hydrologic export in C cycle models might lead to overestimation of SOC stocks by 0.3–2.6 Pg C for the conterminous United States. High uncertainty in hydrologic C export fluxes and potentially substantial effects on soil C turnover illustrate the need for research aimed at improving our mechanistic understanding of the processes regulating hydrologic C export.

Abstract Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin‐up of land carbon cycle models by tens of times. The accelerated spin‐up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever‐increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.

This chapter demonstrates how mathematical models and data-model fusion can be used to identify the processes controlling carbon (C) dynamics.A suite of process-based models was built to describe the C dynamics in a temperate lake.The models were built so as to create a factorial modeling experiment aiming to identify the processes that contributed most to explaining the observed variation in the observed data.All models were calibrated using the Bayesian Markov Chain Monte Carlo algorithm and their fit indices were used to identify the processes that contributed most to model improvement.Although the example used in this lecture focuses on C dynamics in a temperate lake, the presented hypothesis-testing algorithm is transferable to other response variables and ecosystems.

Terrestrial ecosystems can potentially alleviate or exacerbate climate change by regulating atmospheric CO 2 concentration. Divergent predictions of the terrestrial C sink by the Earth System Models (ESMs) indicate no unified mechanism regarding abiotic and biotic response to climate change. The amount and diversity of observations of the terrestrial C cycle create an opportunity to improve the predictive capacity of C‐cycle models. Modeling study with observations could provide valuable insights into the controls on interannual variability of the terrestrial C sink. In this study, we used data from three deciduous forest sites in the National Ecological Observatory Network (NEON) to do site‐specific parameterizations in the Terrestrial ECOsystem model (TECO), and explore controls of the net C uptake. Calibrated TECO explained 58%–83% of variation in leaf area index and 35%–40% in net ecosystem C exchange. Root mean square (percentage) errors were 131–436 gC/m 2 (0.9%–3.3%) for wood C pool, 3–73 gC/m 2 (0.5%–11.5%) for fine root C, and 170–763 gC/m 2 (1.9%–8.0%) for soil C pool. Calibrated parameters revealed site‐specific processes in phenology and turnover of leaves, fine roots, slow‐decomposing soil C, and temperature sensitivity of organic matter decomposition among the three deciduous forest sites. These findings imply the presence of bias in models using parameters at plant functional type (PFT) level, which is the case for many ESMs relying on land surface components (i.e., PFTs), rather than site‐specific parameters. Lastly, the magnitude of the terrestrial C sink in deciduous forests increased with temperature and this increase was caused by the temperature‐driven stimulation of the gross primary production.

Carbon (C) is the major building block of all known life, is chemically versatile, and can form a diverse array of organic and inorganic compounds. This chapter examines the C cycle, starting at the global scale and several time frames, and then focuses on the C cycle in a few exemplar ecosystems to illustrate certain aspects. First, however, the chapter reviews the biogeochemistry of C, and what a biogeochemical cycle is.

Soil respiration, the major pathway for ecosystem carbon (C) loss, has the potential to enter a positive feedback loop with the atmospheric CO2 due to climate warming. For reliable projections of climate-carbon feedbacks, accurate quantification of soil respiration and identification of mechanisms that control its variability are essential. Process-based models simulate soil respiration as functions of belowground C input, organic matter quality, and sensitivity to environmental conditions. However, evaluation and calibration of process-based models against the long-term in situ measurements are rare. Here, we evaluate the performance of the Terrestrial ECOsystem (TECO) model in simulating total and heterotrophic soil respiration measured during a 16-year warming experiment in a mixed-grass prairie; calibrate model parameters against these and other measurements collected during the experiment; and explore whether the mechanisms of C dynamics have changed over the years. Calibrating model parameters against observations of individual years substantially improved model performance in comparison to pre-calibration simulations, explaining 79–86% of variability in observed soil respiration. Interannual variation of the calibrated model parameters indicated increasing recalcitrance of soil C and changing environmental sensitivity of microbes. Overall, we found that (1) soil organic C became more recalcitrant in intact soil compared to root-free soil; (2) warming offset the effects of increasing C recalcitrance in intact soil and changed microbial sensitivity to moisture conditions. These findings indicate that soil respiration may decrease in the future due to C quality, but this decrease may be offset by warming-induced changes in C cycling mechanisms and their responses to moisture conditions.

Soil is the largest terrestrial carbon (C) reservoir and is an important component of climate-carbon feedbacks, potentially sequestering or releasing large amounts CO2 from or to the atmosphere. In global land models soil C dynamics is determined by the long-term balance between C inputs and turnover rates, and the latter are usually a function of soil texture, temperature, and soil moisture, which represents environmental limitation of microbial soil organic carbon (SOC) mineralization. Hydrologic C export is often overlooked in the terrestrial C cycle models, likely because proportionally soils contain a very small amount of C that can be exported with runoff, contributing around 2.9 Pg C yr-1 to aquatic systems globally. However, ignoring hydrologic C export in areas, where it has substantial effect on SOC turnover rate, could result in systematic overestimation of SOC stocks and inaccurate simulation of SOC responses to changing environmental conditions. We combined water quality data from the United States Geological Survey with hydrologic and soil chemistry data products to estimate the relative contribution of hydrologic export to bulk soil turnover rates across the continental USA. The catchment area weighted average of hydrologic export effect on SOC turnover was 5.2%. Hydrologic export accounted for 0-2% of the bulk SOC turnover in arid regions, 2-15% - in forests, and 20-40% - in wetland-rich areas. The SOC stocks generated for the continental U.S. using microbe-mediated turnover alone amounted to 88.3 Pg C and were 15.4% higher than the amount reported in the Harmonized World Soil Database (76.5 Pg C), thus illustrating the importance of accounting for hydrologic C export when simulating SOC dynamics.

Background Forests are an important component of the global carbon (C) cycle and can be net sources or sinks of CO 2 , thus mitigating or exacerbating the effects of anthropogenic greenhouse gas emissions. While forest productivity is often inferred from national-scale yield tables or from satellite products, forest C emissions resulting from dead organic matter decay are usually simulated, therefore it is important to ensure the accuracy and reliability of a model used to simulate organic matter decay at an appropriate scale. National Forest Inventories (NFIs) provide a record of carbon pools in ecosystem components, and these measurements are essential for evaluating rates and controls of C dynamics in forest ecosystems. In this study we combine the observations from the Swiss NFIs and machine learning techniques to quantify the decay rates of the standing snags and downed logs and identify the main controls of dead wood decay. Results We found that wood decay rate was affected by tree species, temperature, and precipitation. Dead wood originating from Fagus sylvatica decayed the fastest, with the residence times ranging from 27 to 54 years at the warmest and coldest Swiss sites, respectively. Hardwoods at wetter sites tended to decompose faster compared to hardwoods at drier sites, with residence times 45–92 and 62–95 years for the wetter and drier sites, respectively. Dead wood originating from softwood species had the longest residence times ranging from 58 to 191 years at wetter sites and from 78 to 286 years at drier sites. Conclusions This study illustrates how long-term dead wood observations collected and remeasured during several NFI campaigns can be used to estimate dead wood decay parameters, as well as gain understanding about controls of dead wood dynamics. The wood decay parameters quantified in this study can be used in carbon budget models to simulate the decay dynamics of dead wood, however more measurements (e.g. of soil C dynamics at the same plots) are needed to estimate what fraction of dead wood is converted to CO 2 , and what fraction is incorporated into soil.

A model framework for national greenhouse gas emission and removal estimation for Canadian peatlands (CaMP v2.0) was developed and tested. It provides a module that can work alongside the upland forest Generic Carbon Budget Model (GCBM) developed to eventually replace the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) as the core model in Canada's National Forest Carbon Monitoring, Accounting and Reporting System. The CaMP (v2.0) provides a simple model foundation that can be applied nationally for 11 different peatland categories. It tracks the growth, turnover and decay in annual time steps of different vegetation components (foliage, branches, stems, and roots of trees, shrubs, sedges and mosses). It uses a Q(10) relationship to model peat C pool decomposition as a function of mean annual temperature, and models methane flux response to deviations in annual water table depth. The CaMP takes a simple approach to modeling hydrology for large spatial scales by using the nationally-available Canadian Fire Weather Index Drought Code to predict long-term and annual water table depth. The CaMP (v2.0) provides the framework needed to model disturbances but only includes wildfire in this version. Model behavior and sensitivity were assessed, and evaluated against observed flux data. Results suggest that the CaMP (v2.0) provides an appropriate structure for large spatial- and temporal-scale estimation of emissions, owing to the model behaving as expected relative to shifts in environmental variables, and to reasonably small mean observed to modeled residuals. Methane was overestimated by the model on average by 6 g C ha(-1) y(-1) (n = 53 years of data across 11 peatland sites), and by 8 g C ha(-1) y(-1) when weighted by site location (n = 12 sites, >= 3 years of data per site). The model overestimated net ecosystem exchange (NEE) by 20 g C ha(-1) y(-1) (n = 36 years of data across 12 peatland sites), and by 2 g C ha(-1) y(-1) when weighted by site location (n = 11 sites, >= 3 years of data per site), and results demonstrate that inter-site variation is greater than temporal variation across NEE measures. Several aspects were identified as requiring further work to increase explained variation in finer-scale emission estimates. Recommendations include further expanding the existing peatland databases to re-calibrate peat decomposition rates and better parameterize NPP rates by region for certain vegetation layers and peatland types, as well as developing a national annual-scale soil temperature model that could serve to replace the air temperature (Q(10)) decay relationship currently used in the CaMP (v2.0). Data gaps that were identified include the need for annualized methane flux datasets with appropriate annual-scale meta-data. Future work is required to include permafrost dynamics, as well as additional natural, and anthropogenic disturbances.