Abstract. Most forests of the world are recovering from a past disturbance. It is well known that forest disturbances profoundly affect carbon stocks and fluxes in forest ecosystems, yet it has been a great challenge to assess disturbance impacts in estimates of forest carbon budgets. Net sequestration or loss of CO 2 by forests after disturbance follows a predictable pattern with forest recovery. Forest age, which is related to time since disturbance, is a useful surrogate variable for analyses of the impact of disturbance on forest carbon. In this study, we compiled the first continental forest age map of North America by combining forest inventory data, historical fire data, optical satellite data and the dataset from NASA's Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project. A companion map of the standard deviations for age estimates was developed for quantifying uncertainty. We discuss the significance of the disturbance legacy from the past, as represented by current forest age structure in different regions of the US and Canada, by analyzing the causes of disturbances from land management and nature over centuries and at various scales. We also show how such information can be used with inventory data for analyzing carbon management opportunities. By combining geographic information about forest age with estimated C dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO 2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status. Finally, we describe how the forest age data can be used in large-scale carbon modeling, both for land-based biogeochemistry models and atmosphere-based inversion models, in order to improve the spatial accuracy of carbon cycle simulations.

Abstract. The use of global three-dimensional (3-D) models with satellite observations of CO 2 in inverse modeling studies is an area of growing importance for understanding Earth's carbon cycle. Here we use the GEOS-Chem model (version 8-02-01) CO 2 mode with multiple modifications in order to assess their impact on CO 2 forward simulations. Modifications include CO 2 surface emissions from shipping (∼ 0.19 Pg C yr −1 ), 3-D spatially-distributed emissions from aviation (∼0.16 Pg C yr −1 ), and 3-D chemical production of CO 2 (∼1.05 Pg C yr −1 ). Although CO 2 chemical production from the oxidation of CO, CH 4 and other carbon gases is recognized as an important contribution to global CO 2 , it is typically accounted for by conversion from its precursors at the surface rather than in the free troposphere. We base our model 3-D spatial distribution of CO 2 chemical production on monthly-averaged loss rates of CO (a key precursor and intermediate in the oxidation of organic carbon) and apply an associated surface correction for inventories that have counted emissions of CO 2 precursors as CO 2 . We also explore the benefit of assimilating satellite observations of CO into GEOS-Chem to obtain an observation-based estimate of the CO 2 chemical source. The CO assimilationCorrespondence to: R. Nassar (ray.nassar@ec.gc.ca) corrects for an underestimate of atmospheric CO abundances in the model, resulting in increases of as much as 24% in the chemical source during May-June 2006, and increasing the global annual estimate of CO 2 chemical production from 1.05 to 1.18 Pg C. Comparisons of model CO 2 with measurements are carried out in order to investigate the spatial and temporal distributions that result when these new sources are added. Inclusion of CO 2 emissions from shipping and aviation are shown to increase the global CO 2 latitudinal gradient by just over 0.10 ppm (∼3%), while the inclusion of CO 2 chemical production (and the surface correction) is shown to decrease the latitudinal gradient by about 0.40 ppm (∼10%) with a complex spatial structure generally resulting in decreased CO 2 over land and increased CO 2 over the oceans. Since these CO 2 emissions are omitted or misrepresented in most inverse modeling work to date, their implementation in forward simulations should lead to improved inverse modeling estimates of terrestrial biospheric fluxes.

Abstract. The net surface exchange of CO 2 for the years 2002-2007 is inferred from 12 181 atmospheric CO 2 concentration data with a time-dependent Bayesian synthesis inversion scheme. Monthly CO 2 fluxes are optimized for 30 regions of the North America and 20 regions for the rest of the globe. Although there have been many previous multiyear inversion studies, the reliability of atmospheric inversion techniques has not yet been systematically evaluated for quantifying regional interannual variability in the carbon cycle. In this study, the global interannual variability of the CO 2 flux is found to be dominated by terrestrial ecosystems, particularly by tropical land, and the variations of regional terrestrial carbon fluxes are closely related to climate variations. These interannual variations are mostly caused by abnormal meteorological conditions in a few months in the year or part of a growing season and cannot be well represented using annual means, suggesting that we should pay attention to finer temporal climate variations in ecosystem modeling. We find that, excluding fossil fuel and biomass burning emissions, terrestrial ecosystems and oceans absorb an average of 3.63 ± 0.49 and 1.94 ± 0.41 Pg C yr −1 , respectively. The terrestrial uptake is mainly in northern land while the tropical and southern lands contribute 0.62 ± 0.47, and 0.67 ± 0.34 Pg C yr −1 to the sink, respectively. In North America, terrestrial ecosystems absorb 0.89 ± 0.18 Pg C yr −1 on average with a strong flux density found in the south-east of the continent.

The evapotranspiration (ET) from all Canadian landmass in 1996 is estimated at daily steps and 1 km resolution using a process model named boreal ecosystem productivity simulator (BEPS). The model is driven by remotely sensed leaf area index and land cover maps as well as soil water holding capacity and daily meteorological data. All the major ET components are considered: transpiration from vegetation, evaporation of canopy‐intercepted rainfall, evaporation from soil, sublimation of snow in winter and in permafrost and glacier areas, and sublimation of canopy‐intercepted snow. In forested areas the transpiration from both the overstory and understory vegetation is modeled separately. The Penman‐Monteith method was applied to sunlit and shaded leaf groups individually in modeling the canopy‐level transpiration, a methodological improvement necessary for forest canopies with considerable foliage clumping. The modeled ET map displays pronounced east‐west and north‐south gradients as well as detailed variations with cover types and vegetation density. It is estimated that for a relative wet year of 1996, the total ET from all Canada's landmass (excluding inland waters) was 2037 km3. If compared with the total precipitation of 5351 km3 based on the data from a medium range meteorological forecast model, the ratio of ET to precipitation was 38%. The ET averaged over Canadian land surface was 228 mm/yr in 1996, partitioned into transpiration of 102 mm yr−1 and evaporation and sublimation of 126 mm yr−1. Forested areas contributed the largest fraction of the total national ET at 59%. Averaged for all cover types, transpiration accounted for 45% of the total ET, while in forested areas, transpiration contributed 51% of ET. Modeled results of daily ET are compared with eddy covariance measurements at three forested sites with a r2 value of 0.61 and a root mean square error of 0.7 mm/day.

Abstract. We infer CO 2 surface fluxes using satellite observations of mid-tropospheric CO 2 from the Tropospheric Emission Spectrometer (TES) and measurements of CO 2 from surface flasks in a time-independent inversion analysis based on the GEOS-Chem model. Using TES CO 2 observations over oceans, spanning 40 • S-40 • N, we find that the horizontal and vertical coverage of the TES and flask data are complementary. This complementarity is demonstrated by combining the datasets in a joint inversion, which provides better constraints than from either dataset alone, when a posteriori CO 2 distributions are evaluated against independent ship and aircraft CO 2 data. In particular, the joint inversion offers improved constraints in the tropics where surface measurements are sparse, such as the tropical forests of South America. Aggregating the annual surface-to-atmosphere fluxes from the joint inversion for the year 2006 yields −1.13±0.21 Pg C for the global ocean, −2.77±0.20 Pg C for the global land biosphere and −3.90±0.29 Pg C for the total global natural flux (defined as the sum of all biospheric, oceanic, and biomass burning contributions but excluding CO 2 emissions from fossil fuel combustion). These global ocean and global land fluxes are shown to be near the median of the broad range of values from other inversion resultsCorrespondence to: R. Nassar (ray.nassar@ec.gc.ca) for 2006. To achieve these results, a bias in TES CO 2 in the Southern Hemisphere was assessed and corrected using aircraft flask data, and we demonstrate that our results have low sensitivity to variations in the bias correction approach. Overall, this analysis suggests that future carbon data assimilation systems can benefit by integrating in situ and satellite observations of CO 2 and that the vertical information provided by satellite observations of mid-tropospheric CO 2 combined with measurements of surface CO 2 , provides an important additional constraint for flux inversions.

[1] Carbon dynamics in peatlands are controlled, in large part, by their wetness as defined by water table depth and volumetric liquid soil moisture content. A common type of peatland is raised bogs that typically have a multiple-layer canopy of vascular plants over a Sphagnum moss ground cover. Their convex form restricts water supply to precipitation and water is shed toward the margins, usually by lateral subsurface flow. The hydraulic gradient for lateral subsurface flow is governed by the peat surface topography at the mesoscale ($200 m to 5 km). To investigate the influence of mesoscale topography on wetness, evapotranspiration (ET), and gross primary productivity (GPP) in a bog during the snow-free period, we compare the outputs of a further developed version of the daily Boreal Ecosystem Productivity Simulator (BEPS) with observations made at the Mer Bleue peatland, located near Ottawa, Canada. Explicitly considering mesoscale topography, simulated total ET and GPP correlate well with measured ET (r = 0.91) and derived gross ecosystem productivity (GEP; r = 0.92). Both measured ET and derived GEP are simulated similarly well when mesoscale topography is neglected, but daily simulated values are systematically underestimated by about 10% and 12% on average, respectively, due to greater wetness resulting from the lack of lateral subsurface flow. Owing to the differences in moss surface conductances of water vapor and carbon dioxide with increasing moss water content, the differences in the spatial patterns of simulated total ET and GPP are controlled by the mesotopographic position of the moss ground cover.

In this study, we establish a nested atmospheric inversion system with a focus on China using the Bayesian method. The global surface is separated into 43 regions based on the 22 TransCom large regions, with 13 small regions in China. Monthly CO₂ concentrations from 130 GlobalView sites and 3 additional China sites are used in this system. The core component of this system is an atmospheric transport matrix, which is created using the TM5 model with a horizontal resolution of 3° × 2°. The net carbon fluxes over the 43 global land and ocean regions are inverted for the period from 2002 to 2008. The inverted global terrestrial carbon sinks mainly occur in boreal Asia, South and Southeast Asia, eastern America and southern South America. Most China areas appear to be carbon sinks, with strongest carbon sinks located in Northeast China. From 2002 to 2008, the global terrestrial carbon sink has an increasing trend, with the lowest carbon sink in 2002. The inter-annual variation (IAV) of the land sinks shows remarkable correlation with the El Niño Southern Oscillation (ENSO). The terrestrial carbon sinks in China also show an increasing trend. However, the IAV in China is not the same as that of the globe. There is relatively stronger land sink in 2002, lowest sink in 2006, and strongest sink in 2007 in China. This IAV could be reasonably explained with the IAVs of temperature and precipitation in China. The mean global and China terrestrial carbon sinks over the period 2002–2008 are −3.20 ± 0.63 and −0.28 ± 0.18 PgC yr⁻¹, respectively. Considering the carbon emissions in the form of reactive biogenic volatile organic compounds (BVOCs) and from the import of wood and food, we further estimate that China's land sink is about −0.31 PgC yr⁻¹

Abstract. Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modelled at the 1 km resolution is reduced from 14.8 g C m−2 yr−1 to 4.8 g C m−2 yr−1 in comparison with NPP modelled at 30 m resolution, where the mean NPP is 668 g C m−2 yr−1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI) and elevation have small and additive effects on improving the spatial scaling between these two resolutions.