Aspects of forest biomass in the earth system: Its role and major unknowns

Forests are a major and diverse land cover occupying a third of the terrestrial vegetated surface; they store 50 to 65% of terrestrial organic carbon (including the soil) and contribute half to terrestrial productivity. Forest biomass stores close to 80% of all the biomass on Earth. As noted earlier, forests play an important role in the Earth system as carbon stocks, carbon sinks, mediator of the water cycle and as modifier of land surface roughness and albedo. Moreover, forests play a role as habitat for many species, are an economic source of timber and firewood and have recreational value for local populations and touristic visitors. Here, we appraise how ecosystem functions are influenced in particular by biomass and its vertical and horizontal distribution and hypothesize that almost all functions are directly or indirectly related to biomass, in addition to other factors. At landscape or regional scale, heterogeneity of biomass presumably has an important influence on a variety of processes, but there are gaps both in quantifying the heterogeneity of forests globally and in quantifying the effect of this heterogeneity. Similarly, while the role of forests for the global carbon cycle is important, large uncertainties exist regarding stocks, turnover times and the carbon sink function in forest, as an analysis of state-of-the-art carbon cycle and vegetation models shows. Upcoming global satellite missions such as GEDI, NISAR and BIOMASS will be able to address the above uncertainties and lack of understanding in combination with modeling approaches, in particular by exploiting information on vertical and horizontal heterogeneity

The environment of AGNs and the activity degree of their surrounding galaxies

Aims. We present results of a comprehensive spectral study on the large-scale environment of AGNs based on Sloan Spectroscopic Survey data. Methods. We analyzed the spectra of galaxies in the environment of AGN and other activity classes up to distances of 1 Mpc. Results. The mean H{\alpha} and [OIII] {\lambda}5007 line luminosities in the environmental galaxies within a projected radius of 1 Mpc are highest around Seyfert 1 galaxies, with decreasing luminosities for Seyfert 2 and HII galaxies, and lowest for absorption line galaxies. Furthermore, there is a trend toward H{\alpha} and [OIII] luminosities in the environmental galaxies increasing as a function of proximity to the central emission line galaxies. There is another clear trend toward a neighborhood effect within a radius of 1000 kpc for the AGN and non-AGN types: Seyfert galaxies tend to have the highest probability of having another Seyfert galaxy in the neighborhood. HII galaxies tend to have the highest probability of having another HII galaxy in the neighborhood, etc. The number of companions within 1000 kpc is inversely correlated with the H{\alpha}, [OIII] {\lambda}5007, as well as with the continuum luminosities of the central galaxies, regardless of whether they are of Seyfert, HII, or absorption line types.Comment: 9 pages, 6 figures, to be published in A&

Nitrification amplifies the decreasing trends of atmospheric oxygen and implies a larger land carbon uptake

[1] Atmospheric O-2 trend measurements are used to partition global oceanic and land biotic carbon sinks on a multiannual basis. The underlying principle is that a terrestrial uptake or release of CO₂ is accompanied by an opposite flux of O-2. The molar ratio of the CO₂ and O-2 terrestrial fluxes should be 1, if no other elements are considered. However, reactive nitrogen produced by human activities (e.g., fertilizers, N deposition) is also being incorporated into plant tissues. The various reaction pathways of the terrestrial nitrogen cycle cause fluxes of atmospheric O-2. Thus the cycles of nitrogen, carbon, and oxygen must be linked together. We report here on previously unconsidered anthropogenic nitrogen-related mechanisms which impact atmospheric O-2 trends and thus the derived global carbon sinks. In particular, we speculate that anthropogenic-driven changes are driving the global nitrogen cycle to a more oxidized state, primarily through nitrification, nitrate fertilizer industrial production, and combustion of fossil fuels and anthropogenic biomass burning. The sum of these nitrogen-related processes acts to additionally decrease atmospheric O-2 and slightly increase atmospheric CO₂. We have calculated that the effective land biotic O-2: CO₂ molar ratio ranges between 0.76 and 1.04 rather than 1.10 ( moles of O-2 produced per mole of CO₂ consumed) over the period 1993 - 2003, depending on which of four contrasting nitrogen oxidation and reduction pathway scenarios is used. Using the scenario in which we have most confidence, this implies a 0.23 PgC yr(-1) correction to the global land biotic and oceanic carbon sinks of most recently reported estimates over 1993 - 2003, with the land biotic sink becoming larger and the oceanic sink smaller. We have attributed large uncertainties of 100% to all nitrogen-related O-2 and CO₂ fluxes and this corresponds up to +/- 0.09 PgC yr(-1) increase in global carbon sink uncertainties. Thus accounting for anthropogenic nitrogen-related terrestrial fluxes of O-2 results in a 45% larger land biotic sink of 0.74 +/- 0.78 PgC yr(-1) and a slightly smaller oceanic sink of 2.01 +/- 0.66 PgC yr(-1) for the decade 1993 - 2003. [References: 38

Soils apart from equilibrium ? consequences for soil carbon balance modelling

International audienceMany projections of the soil carbon sink or source are based on kinetically defined carbon pool models. Para\-meters of these models are often determined in a way that the steady state of the model matches observed carbon stocks. The underlying simplifying assumption is that observed carbon stocks are near equilibrium. This assumption is challenged by observations of very old soils that do still accumulate carbon. In this modelling study we explored the consequences of the case where soils are apart from equilibrium. Calculation of equilibrium states of soils that are currently accumulating small amounts of carbon were performed using the Yasso model. It was found that already very small current accumulation rates cause big changes in theoretical equilibrium stocks, which can virtually approach infinity. We conclude that soils that have been disturbed several centuries ago are not in equilibrium but in a transient state because of the slowly ongoing accumulation of the slowest pool. A first consequence is that model calibrations to current carbon stocks that assume equilibrium state, overestimate the decay rate of the slowest pool. A second consequence is that spin-up runs (simulations until equilibrium) overestimate stocks of recently disturbed sites. In order to account for these consequences, we propose a transient correction. This correction prescribes a lower decay rate of the slowest pool and accounts for disturbances in the past by decreasing the spin-up-run predicted stocks to match an independent estimate of current soil carbon stocks. Application of this transient correction at a Central European beech forest site with a typical disturbance history resulted in an additional carbon fixation of 5.7±1.5 tC/ha within 100 years. Carbon storage capacity of disturbed forest soils is potentially much higher than currently assumed. Simulations that do not adequately account for the transient state of soil carbon stocks neglect a considerable amount of current carbon accumulation

Summarizing the state of the terrestrial biosphere in few dimensions

In times of global change, we must closely monitor the state of the planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems – i.e. the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73 % of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We assume that this generic approach has great potential for the analysis of land-surface dynamics from observational or model data

Influences of observation errors in eddy flux data on inverse model parameter estimation

Eddy covariance data are increasingly used to estimate parameters of ecosystem models. For proper maximum likelihood parameter estimates the error structure in the observed data has to be fully characterized. In this study we propose a method to characterize the random error of the eddy covariance flux data, and analyse error distribution, standard deviation, cross- and autocorrelation of CO<sub>2</sub> and H<sub>2</sub>O flux errors at four different European eddy covariance flux sites. Moreover, we examine how the treatment of those errors and additional systematic errors influence statistical estimates of parameters and their associated uncertainties with three models of increasing complexity – a hyperbolic light response curve, a light response curve coupled to water fluxes and the SVAT scheme BETHY. In agreement with previous studies we find that the error standard deviation scales with the flux magnitude. The previously found strongly leptokurtic error distribution is revealed to be largely due to a superposition of almost Gaussian distributions with standard deviations varying by flux magnitude. The crosscorrelations of CO<sub>2</sub> and H<sub>2</sub>O fluxes were in all cases negligible (<i>R</i><sup>2</sup> below 0.2), while the autocorrelation is usually below 0.6 at a lag of 0.5 h and decays rapidly at larger time lags. This implies that in these cases the weighted least squares criterion yields maximum likelihood estimates. To study the influence of the observation errors on model parameter estimates we used synthetic datasets, based on observations of two different sites. We first fitted the respective models to observations and then added the random error estimates described above and the systematic error, respectively, to the model output. This strategy enables us to compare the estimated parameters with true parameters. We illustrate that the correct implementation of the random error standard deviation scaling with flux magnitude significantly reduces the parameter uncertainty and often yields parameter retrievals that are closer to the true value, than by using ordinary least squares. The systematic error leads to systematically biased parameter estimates, but its impact varies by parameter. The parameter uncertainty slightly increases, but the true parameter is not within the uncertainty range of the estimate. This means that the uncertainty is underestimated with current approaches that neglect selective systematic errors in flux data. Hence, we conclude that potential systematic errors in flux data need to be addressed more thoroughly in data assimilation approaches since otherwise uncertainties will be vastly underestimated

Carbon balance assessment of a natural steppe of southern Siberia by multiple constraint approach

Steppe ecosystems represent an interesting case in which the assessment of carbon balance may be performed through a cross validation of the eddy covariance measurements against ecological inventory estimates of carbon exchanges (Ehman et al., 2002; Curtis et al., 2002). <br><br> Indeed, the widespread presence of ideal conditions for the applicability of the eddy covariance technique, as vast and homogeneous grass vegetation cover over flat terrains (Baldocchi, 2003), make steppes a suitable ground to ensure a constrain to flux estimates with independent methodological approaches. <br><br> We report about the analysis of the carbon cycle of a true steppe ecosystem in southern Siberia during the growing season of 2004 in the framework of the TCOS-Siberia project activities performed by continuous monitoring of CO<sub>2</sub> fluxes at ecosystem scale by the eddy covariance method, fortnightly samplings of phytomass, and ingrowth cores extractions for NPP assessment, and weekly measurements of heterotrophic component of soil CO<sub>2</sub> effluxes obtained by an experiment of root exclusion. <br><br> The carbon balance of the monitored natural steppe was, according to micrometeorological measurements, a sink of carbon of 151.7±36.9 g C m<sup>−2</sup>, cumulated during the growing season from May to September. This result was in agreement with the independent estimate through ecological inventory which yielded a sink of 150.1 g C m<sup>−2</sup> although this method was characterized by a large uncertainty (±130%) considering the 95% confidence interval of the estimate. Uncertainties in belowground process estimates account for a large part of the error. Thus, in particular efforts to better quantify the dynamics of root biomass (growth and turnover) have to be undertaken in order to reduce the uncertainties in the assessment of NPP. This assessment should be preferably based on the application of multiple methods, each one characterized by its own merits and flaws

Characterizing Ecosystem-Atmosphere Interactions from Short to Interannual Time Scales

Characterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series are now partly comprising an observation 5 period of one decade. In this study, we explored whether the observation period is already sufficient to detect cross relationships of the variables beyond the annual cycle as they are expected from comparable studies in climatology. We explored the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an 10 effective signal to noise separation. We found that most observations (NEE, GP P , Reco, V P D, LE, H, u, P ) were influenced significantly by low frequency components (interannual variability). Furthermore we extracted a set of nonlinear relationships and found clear annual hysteresis effects except for the NEE-Rg relationship which turned out to be the sole linear relationship 15 in the observation space. SSA provides a new tool to investigate these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing since it can be applied as novel gap fillingapproach relying on the temporal time series structure only.JRC.H.2-Climate chang