The major driving factors of soil CO2 production - substrate supply, temperature, and water content - vary vertically within the soil profile, with the greatest temporal variations of these factors usually near the soil surface. Several studies have demonstrated that wetting and drying of the organic horizon contributes to temporal variation in summertime soil CO2 efflux in forests, but this contribution is difficult to quantify. The objectives of this study were to partition CO2 production vertically in a mixed hardwood stand of the Harvard Forest, Massachusetts, USA, and then to use that partitioning to evaluate how the relative contributions of CO2 production by genetic soil horizon vary seasonally and interannually. We measured surface CO2 efflux and vertical soil profiles of CO2 concentration, temperature, water content, and soil physical characteristics. These data were applied to a model of effective diffusivity to estimate CO2 flux at the top of each genetic soil horizon and the production within each horizon. A sensitivity analysis revealed sources of uncertainty when applying a diffusivity model to a rocky soil with large spatial heterogeneity, especially estimates of bulk density and volumetric water content and matching measurements of profiles and surface fluxes. We conservatively estimate that the O horizon contributed 40-48% of the total annual soil CO2 efflux. Although the temperature sensitivity of CO2 production varied across soil horizons, the partitioning of CO2 production by horizon did not improve the overall prediction of surface CO2 effluxes based on temperature functions. However, vertical partitioning revealed that water content covaried with CO2 production only in the O horizon. Large interannual variations in estimates of O horizon CO2 production indicate that this layer could be an important transient interannual source or sink of ecosystem C. © 2006 Blackwell Publishing Ltd.

Currently, forests in the northeastern United States are net sinks of atmospheric carbon. Under future climate change scenarios, the combined effects of climate change and nitrogen deposition on soil decomposition, aboveground processes, and the forest carbon balance remain unclear. We applied carbon stock, flux, and isotope data from field studies at the Harvard forest, Massachusetts, to the ForCent model, which integrates above- and belowground processes. The model was able to represent decadal-scale measurements in soil C stocks, mean residence times, fluxes, and responses to a warming and N addition experiment. The calibrated model then simulated the longer term impacts of warming and N deposition on the distribution of forest carbon stocks. For simulation to 2030, soil warming resulted in a loss of soil organic matter (SOM), decreased allocation to belowground biomass, and gain of aboveground carbon, primarily in large wood, with an overall small gain in total system carbon. Simulated nitrogen addition resulted in a small increase in belowground carbon pools, but a large increase in aboveground large wood pools, resulting in a substantial increase in total system carbon. Combined warming and nitrogen addition simulations showed a net gain in total system carbon, predominately in the aboveground carbon pools, but offset somewhat by losses in SOM. Hence, the impact of continuation of anthropogenic N deposition on the hardwood forests of the northeastern United States may exceed the impact of warming in terms of total ecosystem carbon stocks. However, it should be cautioned that these simulations do not include some climate-related processes, different responses from changing tree species composition. Despite uncertainties, this effort is among the first to use decadal-scale observations of soil carbon dynamics and results of multifactor manipulations to calibrate a model that can project integrated aboveground and belowground responses to nitrogen and climate changes for subsequent decades.

Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO2), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO₂ flux, commonly though imprecisely termed soil respiration (R_S ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency R_S measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured R_S , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.