Approaches for Carbon Budget Analyses of the Siberian Forests

This report carried out by Timo Karjalainen and Jari Liski is a contribution to the analyses of carbon balances of the Siberian forests. The report contains two inter-linked sections. The aim of the first section was to develop a method to assess carbon budget for tree biomass at ecoregional level in Siberia. Tree biomass carbon budgets contain estimates on the initial amounts of carbon in the whole tree biomass, as well as its dynamics. The calculation method developed based on the structure of the Russian forest database at IIASA and available models describing tree growth and biomass allocation in Siberia. Calculated litter production is used as input for separate calculations on the soil organic matter carbon budget (Liski, 1997). Stand level analyses showed that the developed method describes vegetation carbon budget in a plausible manner. The stand level analyses are the platform for regional assessments. There are, however, several matters that should be taken into account in the regional assessments. These relate to stand structure, description of stand replacing disturbances, and availability of data. In the second section, different models describing the dynamics of organic C in forest soils were developed and then compared. The model judged to describe the dynamics of soil C in the most realistic way contains five compartments for different litter and three for soil organic matter (the so called soil C model). Temperature was considered the most important climatic factor that regulates the decomposition in boreal forests. The effective temperature sum with a +5 degree C threshold was chosen to describe the temperature impact on the decomposition. The application of the developed models was tested on the issues of impacts of species, harvesting intervals and harvesting residues left on the site. For a full-scale application of the developed soil carbon models for Siberia and Russia, the special features of permafrost soils and peatlands need to be added to the models

Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO₂

The trajectories of soil carbon (C) in the changing climate are of utmost importance, as soil carbon is a substantial carbon storage with a large potential to impact the atmospheric carbon dioxide (CO2) burden. Atmospheric CO2 observations integrate all processes affecting C exchange between the surface and the atmosphere. Therefore they provide a benchmark for carbon cycle models. We evaluated two distinct soil carbon models (CBALANCE and YASSO) that were implemented to a global land surface model (JSBACH) against atmospheric CO2 observations. We transported the biospheric carbon fluxes obtained by JSBACH using the atmospheric transport model TM5 to obtain atmospheric CO2. We then compared these results with surface observations from Global Atmosphere Watch (GAW) stations as well as with column XCO2 retrievals from the GOSAT satellite. The seasonal cycles of atmospheric CO2 estimated by the two different soil models differed. The estimates from the CBALANCE soil model were more in line with the surface observations at low latitudes (0 N–45 N) with only 1 % bias in the seasonal cycle amplitude (SCA), whereas YASSO was underestimating the SCA in this region by 32 %. YASSO gave more realistic seasonal cycle amplitudes of CO2 at northern boreal sites (north of 45 N) with underestimation of 15 % compared to 30 % overestimation by CBALANCE. Generally, the estimates from CBALANCE were more successful in capturing the seasonal patterns and seasonal cycle amplitudes of atmospheric CO2 even though it overestimated soil carbon stocks by 225 % (compared to underestimation of 36 % by YASSO) and its predictions of the global distribution of soil carbon stocks was unrealistic. The reasons for these differences in the results are related to the different environmental drivers and their functional dependencies of these two soil carbon models. In the tropical region the YASSO model showed earlier increase in season of the heterotophic respiration since it is driven by precipitation instead of soil moisture as CBALANCE. In the temperate and boreal region the role of temperature is more dominant. There the heterotophic respiration from the YASSO model had larger annual variability, driven by air temperature, compared to the CBALANCE which is driven by soil temperature. The results underline the importance of using sub-yearly data in the development of soil carbon models when they are used in shorter than annual time scales

Soil carbon monitoring using surveys and modelling. General description and application in the United Republic of Tanzania

This publication describes the application of survey- and modelling-based methods for monitoring soil organic carbon stock and its changes on a national scale. The report presents i) a design of the first inventory of soil organic carbon, including discussion on factors that affect the reliability of carbon stock estimates; and ii) a design of a modelling-based approach, including links to national forest inventory data and discussion on alternative soil organic carbon models. Both approaches can provide necessary information on soil carbon changes for a national greenhouse gas (GHG) inventory. Forest soils constitute a large pool of carbon and releases of carbon from this pool, caused by anthropogenic activities such as deforestation and forest degradation, may significantly increase the concentration of GHGs in the atmosphere. Therefore, estimating and reducing emissions from these activities have become timely issues. Currently, reliable estimates of soil organic carbon stock and stock changes are needed for REDO (Reducing Emissions from Deforestation and Forest Degradation in Developing Countries) and GHG reporting under the United Nations Framework Convention on Climate Change (UNFCCC).The document is available in print formMinistry for foreign affairs of Finlan

Leaf litter decomposition -- Estimates of global variability based on Yasso07 model

Litter decomposition is an important process in the global carbon cycle. It accounts for most of the heterotrophic soil respiration and results in formation of more stable soil organic carbon (SOC) which is the largest terrestrial carbon stock. Litter decomposition may induce remarkable feedbacks to climate change because it is a climate-dependent process. To investigate the global patterns of litter decomposition, we developed a description of this process and tested the validity of this description using a large set of foliar litter mass loss measurements (nearly 10 000 data points derived from approximately 70 000 litter bags). We applied the Markov chain Monte Carlo method to estimate uncertainty in the parameter values and results of our model called Yasso07. The model appeared globally applicable. It estimated the effects of litter type (plant species) and climate on mass loss with little systematic error over the first 10 decomposition years, using only initial litter chemistry, air temperature and precipitation as input variables. Illustrative of the global variability in litter mass loss rates, our example calculations showed that a typical conifer litter had 68% of its initial mass still remaining after two decomposition years in tundra while a deciduous litter had only 15% remaining in the tropics. Uncertainty in these estimates, a direct result of the uncertainty of the parameter values of the model, varied according to the distribution of the litter bag data among climate conditions and ranged from 2% in tundra to 4% in the tropics. This reliability was adequate to use the model and distinguish the effects of even small differences in litter quality or climate conditions on litter decomposition as statistically significant.Comment: 19 Pages, to appear in Ecological Modellin

Mechanism Design in Social Networks

This paper studies an auction design problem for a seller to sell a commodity in a social network, where each individual (the seller or a buyer) can only communicate with her neighbors. The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue. We propose a novel auction mechanism, called information diffusion mechanism (IDM), which incentivizes the buyers to not only truthfully report their valuations on the commodity to the seller, but also further propagate the auction information to all their neighbors. In comparison, the direct extension of the well-known Vickrey-Clarke-Groves (VCG) mechanism in social networks can also incentivize the information diffusion, but it will decrease the seller's revenue or even lead to a deficit sometimes. The formalization of the problem has not yet been addressed in the literature of mechanism design and our solution is very significant in the presence of large-scale online social networks.Comment: In The Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, US, 04-09 Feb 201

A Virtual Tube Delay Effect

A virtual tube delay effect based on the real-time simulation of acoustic wave propagation in a garden hose is presented. The paper describes the acoustic measurements conducted and the analysis of the sound propagation in long narrow tubes. The obtained impulse responses are used to design delay lines and digital filters, which simulate the propagation delay, losses, and reflections from the end of the tube which may be open, closed, or acoustically attenuated. A study on the reflection caused by a finite-length tube is described. The resulting system consists of a digital waveguide model and produces delay effects having a realistic low-pass filtering. A stereo delay effect plugin in PURE DATA has been implemented and it is described here