We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.

[1] Field-chamber measurements of soil respiration from 17 different forest and shrubland sites in Europe and North America were summarized and analyzed with the goal to develop a model describing seasonal, interannual and spatial variability of soil respiration as affected by water availability, temperature, and site properties. The analysis was performed at a daily and at a monthly time step. With the daily time step, the relative soil water content in the upper soil layer expressed as a fraction of field capacity was a good predictor of soil respiration at all sites. Among the site variables tested, those related to site productivity (e.g., leaf area index) correlated significantly with soil respiration, while carbon pool variables like standing biomass or the litter and soil carbon stocks did not show a clear relationship with soil respiration. Furthermore, it was evidenced that the effect of precipitation on soil respiration stretched beyond its direct effect via soil moisture. A general statistical nonlinear regression model was developed to describe soil respiration as dependent on soil temperature, soil water content, and site-specific maximum leaf area index. The model explained nearly two thirds of the temporal and intersite variability of soil respiration with a mean absolute error of 0.82 mmol m À2 s À1. The parameterized model exhibits the following principal properties: (1) At a relative amount of upper-layer soil water of 16% of field capacity, half-maximal soil respiration rates are reached. (2) The apparent temperature sensitivity of soil respiration measured as Q 10 varies between 1 and 5 depending on soil temperature and water content. (3) Soil respiration under reference moisture and temperature conditions is linearly related to maximum site leaf area index. At a monthly timescale, we employed the approach by Raich et al. [2002] that used monthly precipitation and air temperature to globally predict soil respiration (T&P model). While this model was able to explain some of the month-to-month variability of soil respiration, it failed to capture the intersite variability, regardless of whether the original or a new optimized model parameterization was used. In both cases, the residuals were strongly related to maximum site leaf area GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 17, NO. 4, 1104, doi:10.1029/2003GB002035, 2003 15 -1 index. Thus, for a monthly timescale, we developed a simple T&P&LAI model that includes leaf area index as an additional predictor of soil respiration. This extended but still simple model performed nearly as well as the more detailed time step model and explained 50% of the overall and 65% of the site-to-site variability. Consequently, better estimates of globally distributed soil respiration should be obtained with the new model driven by satellite estimates of leaf area index. Before application at the continental or global scale, this approach should be further tested in boreal, cold-temperate, and tropical biomes as well as for non-woody vegetation.INDEX TERMS: 1615 Global...

Recently flux tower data have become available for a variety of ecosystems under different climatic and edaphic conditions. Although Flux tower data represent point measurements with a footprint of typically 1 km × 1 km they can be used to validate models and to spatialize biospheric fluxes at regional and continental scales. In this paper we present a study where biospheric flux data collected in the EUROFLUX project were used to train a neural network simulator to provide spatial (1 km × 1 km) and temporal (weekly) estimates of carbon fluxes of European forests at continental scale. The novelty of the approach is that flux data were used to constrain and parameterize the neural network structure using a limited number of input driving variables. The overall European carbon uptake from this analysis was 0.47 Gt C yr−1 with distinctive differences between boreal and temperate regions. The length of the growing season is longer in the south of Europe (about 32 weeks), compared with north and central Europe, which have a similar length‐growing season (about 27 weeks). A peak in respiration was depicted in spring at continental scale as a coherent signal which parallel the construction respiration increase at the onset of the season as usually shown by leaf level measurements.

[1] Forests around Manaus have staged the oldest and the longest forest-atmosphere CO 2 exchange studies made anywhere in the Amazon. Since July 1999 the exchange of CO 2 , water, and energy, as well as weather variables, have been measured almost continuously over two forests, 11 km apart, in the Cuieiras reserve near Manaus, Brazil. This paper presents the sites and climatology of the region based upon the new data sets. The landscape consists of plateaus dissected by often waterlogged valleys, and the two sites differ in terms of the relative areas of those two landscape components represented in the tower footprints. The radiation and wind climate was similar to both towers. Generally, both the long-wave and short-wave radiation input was less in the wet than in the dry season. The energy balance closure was imperfect (on average 80%) in both towers, with little variation in energy partitioning between the wet and dry seasons; likely a result of anomalously high rainfall in the 1999 dry season. Fluxes of CO 2 also showed little seasonal variation except for a slightly shorter daytime uptake duration and somewhat lower respiratory fluxes in the dry season. The net effect is one of lower daily net ecosystem exchange (NEE) in the dry season. The tower, which has less waterlogged valley areas in its footprint, measured a higher overall CO 2 uptake rate. We found that on first sight, NEE is underestimated during calm nights, as was observed in many other tower sites before. However, a closer inspection of the diurnal variation of CO 2 storage fluxes and NEE suggests that at least part of the nighttime deficits is recovered from either lateral influx of CO 2 from valleys or outgassing of soil storage. Therefore there is a high uncertainty in the magnitude of nocturnal NEE, and consequently preliminary estimates of annual carbon uptake reflecting this range from 1 to 8 T ha À1 y À1 , with an even higher upper range for the less waterlogged area. The high uptake rates are clearly unsustainable and call for further investigations into the integral carbon balance of Amazon landscapes.

[1] The warm season (mid-June through late August) partitioning between sensible (H ) and latent (LE ) heat flux, or the Bowen ratio (b = H/LE ), was investigated at 27 sites over 66 site years within the international network of eddy covariance sites (FLUXNET). Variability in b across ecosystems and climates was analyzed by quantifying general climatic and surface characteristics that control flux partitioning. The climatic control on b was quantified using the climatological resistance (R i ), which is proportional to the ratio of vapor pressure deficit (difference between saturation vapor pressure and atmospheric vapor pressure) to net radiation (large values of R i decrease b). The control of flux partitioning by the vegetation and underlying surface was quantified by computing the surface resistance to water vapor transport (R c , with large values tending to increase b).There was a considerable range in flux partitioning characteristics (R c , R i and b) among sites, but it was possible to define some general differences between vegetation types and climates. Deciduous forest sites and the agricultural site had the lowest values of R c and b (0.25-0.50). Coniferous forests typically had a larger R c and higher b (typically between 0.50 and 1.00 but also much larger). However, there was notable variability in R c and R i between coniferous site years, most notably differences between oceanic and continental climates and sites with a distinct dry summer season (Mediterranean climate). Sites with Mediterranean climates generally had the highest net radiation, R c , R i , and b. There was considerable variability in b between grassland site years, primarily the result of interannual differences in soil water content and R c .