Abstract. The turnover time of terrestrial ecosystem carbon is an emergent ecosystem property that quantifies the strength of land surface on the global carbon cycle–climate feedback. However, observation- and modeling-based estimates of carbon turnover and its response to climate are still characterized by large uncertainties. In this study, by assessing the apparent whole ecosystem carbon turnover times (τ) as the ratio between carbon stocks and fluxes, we provide an update of this ecosystem level diagnostic and its associated uncertainties in high spatial resolution (0.083∘) using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble of data, we estimated the global median τ to be 43-7+7 yr (median-difference to percentile 25+difference to percentile 75) when the full soil is considered, in contrast to limiting it to 1 m depth. Only considering the top 1 m of soil carbon in circumpolar regions (assuming maximum active layer depth is up to 1 m) yields a global median τ of 37-6+3 yr, which is longer than the previous estimates of 23-4+7 yr (Carvalhais et al., 2014). We show that the difference is mostly attributed to changes in global Csoil estimates. Csoil accounts for approximately 84 % of the total uncertainty in global τ estimates; GPP also contributes significantly (15 %), whereas Cveg contributes only marginally (less than 1 %) to the total uncertainty. The high uncertainty in Csoil is reflected in the large range across state-of-the-art data products, in which full-depth Csoil spans between 3362 and 4792 PgC. The uncertainty is especially high in circumpolar regions with an uncertainty of 50 % and a low spatial correlation between the different datasets (0.2<r<0.5) when compared to other regions (0.6<r<0.8). These uncertainties cast a shadow on current global estimates of τ in circumpolar regions, for which further geographical representativeness and clarification on variations in Csoil with soil depth are needed. Different GPP estimates contribute significantly to the uncertainties of τ mainly in semiarid and arid regions, whereas Cveg causes the uncertainties of τ in the subtropics and tropics. In spite of the large uncertainties, our findings reveal that the latitudinal gradients of τ are consistent across different datasets and soil depths. The current results show a strong ensemble agreement on the negative correlation between τ and temperature along latitude that is stronger in temperate zones (30–60∘ N) than in the subtropical and tropical zones (30∘ S–30∘ N). Additionally, while the strength of the τ–precipitation correlation was dependent on the Csoil data source, the latitudinal gradients also agree among different ensemble members. Overall, and despite the large variation in τ, we identified robust features in the spatial patterns of τ that emerge beyond the differences stemming from the data-driven estimates of Csoil, Cveg and GPP. These robust patterns, and associated uncertainties, can be used to infer τ–climate relationships and for constraining contemporaneous behavior of Earth system models (ESMs), which could contribute to uncertainty reductions in future projections of the carbon cycle–climate feedback. The dataset of τ is openly available at https://doi.org/10.17871/bgitau.201911 (Fan et al., 2019).

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Abstract. We develop a one-dimensional (1-D) steady-state isotope marine boundary layer (MBL) model that includes meteorologically important features missing in models of the Craig and Gordon type, namely height-dependent diffusion and mixing, lifting to deliver air to the free troposphere, and convergence of subsiding air. Kinetic isotopic fractionation results from this height-dependent diffusion that starts as pure molecular diffusion at the air–water interface and increases with height due to turbulent eddies. Convergence causes mixing of dry, isotopically depleted air with ambient air. Model results fill a quadrilateral in δD–δ18O space, of which three boundaries are defined by (1) vapor in equilibrium with various sea surface temperatures (SSTs), (2) mixing of vapor in equilibrium with seawater and vapor in subsiding air, and (3) vapor that has experienced maximum possible kinetic fractionation. Model processes also cause variations in d-excess of MBL vapor. In particular, mixing of relatively high d-excess descending and converging air into the MBL increases d-excess, even without kinetic isotope fractionation. The model is tested by comparison with seven data sets of marine vapor isotopic ratios, with excellent correspondence. About 95 % of observational data fall within the quadrilateral predicted by the model. The distribution of observations also highlights the significant influence of vapor from nearby converging descending air on isotopic variations within the MBL. At least three factors may explain the ∼5 % of observations that fall slightly outside of the predicted regions in δD–δ18O and d-excess–δ18O space: (1) variations in seawater isotopic ratios, (2) variations in isotopic composition of subsiding air, and (3) influence of sea spray.

&lt;p&gt;The large uncertainty characterizing the terrestrial carbon (C) cycle is a consequence of the sparse and irregular observations on the ground. In terms of observations, spaceborne remote sensing has been achieving global, repeated coverages of the Earth since the late 1970s, with a continuous increase in terms of density of observations in time and spatial resolution, thus potentially qualifying as data source to fill such gap in knowledge. Above-ground biomass is a baseline for quantifying the terrestrial C pool; however, remote sensing observations do not measure the organic mass of vegetation. Above-ground biomass (AGB) of forests can only be inferred by inverting numerical models relating and combining multiple remote sensing observations. One of the longest time record of observations from space is represented by the backscattered intensity from the European Remote Sensing Wind Scatterometer (ERS WindScat) and the MetOp Advanced Scatterometer (ASCAT), both operating at C-band (wavelength of 6 cm). An almost unbroken time series of backscatter observations at 0.25&amp;#176; spatial resolution exists since 1991 and data continuity is guaranteed in the next decades. In spite of the weak sensitivity of C-band backscatter to AGB, wall-to-wall estimates of AGB have been derived from high-resolution SAR observations by exploiting multiple observations acquired in a relatively short time period&amp;#160; (Santoro et al., Rem. Sens. Env., 2011; Santoro et al., Rem. Sens. Env., 2015). We have now applied this approach to generate a global time series of AGB estimates for each year between 1992 and 2018 from the C-band scatterometer data at 0.25&amp;#176; spatial resolution. The spatial patterns of AGB match known patterns from in situ records and other remote sensing datasets. The uncertainty of our AGB estimates is between 30% and 40% of the estimated value at the pixel level, providing strong confidence in multi-decadal AGB trends. We identify a constant increase of biomass across most boreal and temperate forests of the northern hemisphere. In contrast, we detect severe loss of biomass throughout the wet tropics during the 1990s and the beginning of the 2000 decade in consequence of massive deforestation. This loss in biomass is followed by a steady increase during the 2000s and the beginning of the most recent decade, coming more recently into saturation. Overall, we find that the global AGB density at 0.25&amp;#176; steadily increased by 9% from 71.8 Mg ha&lt;sup&gt;-1&lt;/sup&gt; Pg in the 1990s to 78.1 Mg ha&lt;sup&gt;-1&lt;/sup&gt; in the 2010s. Combining our AGB density estimates with the annual maps of the Climate Change Initiative (CCI) Land Cover dataset, we show that total AGB in forests decreased slightly from 566 Pg in the 1990s to 560 Pg in the 2000s, then increased to 593 Pg in the 2010s, resulting in an almost 5% net increase during the last three decades.&lt;/p&gt;