Systematic, operational, long-term observations of the terrestrial carbon cycle (including its interactions with water, energy and nutrient cycles and ecosystem dynamics) are important for the prediction and management of climate, water resources, food resources, biodiversity and desertification. To contribute to these goals, a terrestrial carbon observing system requires the synthesis of several kinds of observation into terrestrial biosphere models encompassing the coupled cycles of carbon, water, energy and nutrients. Relevant observations include atmospheric composition (concentrations of CO 2 and other gases); remote sensing; flux and process measurements from intensive study sites; in situ vegetation and soil monitoring; weather, climate and hydrological data; and contemporary and historical data on land use, land use change and disturbance (grazing, harvest, clearing, fire). A review of model-data synthesis tools for terrestrial carbon observation identifies 'nonsequential' and 'sequential' approaches as major categories, differing according to whether data are treated all at once or sequentially. The structure underlying both approaches is reviewed, highlighting several basic commonalities in formalism and data requirements.An essential commonality is that for all model-data synthesis problems, both nonsequential and sequential, data uncertainties are as important as data values themselves and have a comparable role in determining the outcome.Given the importance of data uncertainties, there is an urgent need for soundly based uncertainty characterizations for the main kinds of data used in terrestrial carbon observation. The first requirement is a specification of the main properties of the error covariance matrix.As a step towards this goal, semi-quantitative estimates are made of the main properties of the error covariance matrix for four kinds of data essential for terrestrial carbon observation: remote sensing of land surface properties, atmospheric composition measurements, direct flux measurements, and measurements of carbon stores.
Aim To infer a forest carbon density map at 0.01°resolution from a radar remote sensing product for the estimation of carbon stocks in Northern Hemisphere boreal and temperate forests.
LocationThe study area extends from 30°N to 80°N, covering three forest biomes -temperate broadleaf and mixed forests (TBMF), temperate conifer forests (TCF) and boreal forests (BFT) -over three continents (North America, Europe and Asia).
MethodsThis study is based on a recently available growing stock volume (GSV) product retrieved from synthetic aperture radar data. Forest biomass and spatially explicit uncertainty estimates were derived from the GSV using existing databases of wood density and allometric relationships between biomass compartments (stem, branches, roots, foliage). We tested the resultant map against inventorybased biomass data from Russia, Europe and the USA prior to making intercontinent and interbiome carbon stock comparisons.Results Our derived carbon density map agrees well with inventory data at regional scales (r 2 = 0.70-0.90). While 40.7 ± 15.7 petagram of carbon (Pg C) are stored in BFT, TBMF and TCF contain 24.5 ± 9.4 Pg C and 14.5 ± 4.8 Pg C, respectively. In terms of carbon density, we found 6.21 ± 2.07 kg C m −2 retained in TCF and 5.80 ± 2.21 kg C m −2 in TBMF, whereas BFT have a mean carbon density of 4.00 ± 1.54 kg C m −2 . Indications of a higher carbon density in Europe compared with the other continents across each of the three biomes could not be proved to be significant.
Main conclusionsThe presented carbon density and corresponding uncertainty map give an insight into the spatial patterns of biomass and stand as a new benchmark to improve carbon cycle models and carbon monitoring systems. In total, we found 79.8 ± 29.9 Pg C stored in northern boreal and temperate forests, with Asian BFT accounting for 22.1 ± 8.3 Pg C.
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