Estimating the carbon balance of central Siberia using a landscape-ecosystem approach, atmospheric inversion and Dynamic Global Vegetation Models

Abstract

Northern Eurasia is the largest terrestrial reservoir of carbon, and its dynamics and interactions with climate are globally significant. We present five independent estimates of the contemporary carbon balance of central Siberia using three different methodologies: a landscape-ecosystem approach (LEA) that amalgamates comprehensive vegetation, soil, hydrological and morphological information into a Geographical Information System, linked to regression-based estimates of carbon flux; two Dynamic Global Vegetation Models (DGVMs); and two atmospheric inversions. Apart from one of the DGVMs, all methods produce estimates of the net biome productivity (NBP) that are consistent both amongst themselves and with a range of other estimates. They indicate the region to be a carbon sink with a NBP of 27.5 +/- 7.2 g C m-2 yr-1, which is equivalent to 352 +/- 92 Mt C yr-1 if considered representative for boreal Asia. This is comparable with fossil fuel emissions for the Russian Federation, currently estimated as 427 MtC yr-1, and implies that boreal Asia does not play the major role in the northern hemisphere land sink, typically estimated to be of magnitude 1.5-2.9 Gt C yr-1. The LEA and DGVM approaches produce very different partitioning of NBP into its component fluxes. The DGVMs find net primary production (NPP) to be nearly balanced by heterotrophic respiration, disturbance being a relatively small term pushing the system closer to equilibrium. In the LEA, heterotrophic respiration is significantly less than NPP, and disturbance plays a much larger role in the overall carbon balance. The use in the LEA of observationally based estimates of heterotrophic respiration and fire disturbance, along with a more complete description of disturbance fluxes, suggests that the partitioning derived by the LEA is more likely, and that improved process descriptions and constraints by data are needed in the DGVMs

Impact of Siberian observations on the optimization of surface CO<sub>2</sub> flux

“…Especially Siberia is considered to be the one of the largest CO 2 uptake regions and reservoirs due to its forest area (Schulze et al, 1999;Houghton et al, 2007;Tarnocai et al, 2009;Kurganova et al, 2010;Schepaschenko et al, 2011) and the global significance of its dynamics and interactions with the climate (Quegan et al, 2011). Therefore, it is important to accurately estimate the surface CO 2 fluxes in this region.…”

Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Québec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data

“…For the inter-comparison, the data were rasterized to 100 m, averaged to 1 km and resampled to 0.01° pixel size. A second dataset was a regional vegetation database covering the entire study region [39] (Tables 2 and 3). The polygons of the vegetation database were delineated by Russian regional forest inventory and vegetation experts using aerial photographs.…”

“…Although such datasets can only be considered for comparison purposes as they do not qualify as reference sets for validation, they were of interest to benchmark the ASAR GSV and assess its overall reliability towards quantifying forest resources and carbon stocks in the boreal zone and to pinpoint areas of discrepancy. Table 2 provides an overview of the three different types of GSV datasets for the three study regions ( [6,7,10,29,39,[55][56][57]). If necessary, datasets were re-projected and resampled into the geographic projection used for the ASAR data.…”

“…Furthermore, aggregated estimates at the scale of 0.1° and 0.5° were obtained and evaluated. These latter scales are commonly used by biogeochemical models to predict carbon pools [39]. The contribution of ASAR estimates of GSV in the context of current knowledge of carbon stocks in the three study regions was assessed by comparing the results against several datasets of spatially explicit estimates of GSV or above-ground biomass, and official statistics at regional and national level.…”

Towards harmonizing competing models: Russian forests' net primary production case study

“…For example, Quegan et al (2011) suggest weighting prior Gaussian estimates inverse proportionally to their variances within the Bayesian approach; this results in a posterior Gaussian estimate to which terms with higher uncertainty (variance) contribute less that those which are more certain. 2…”