Does Terrestrial Drought Explain Global CO2 Flux Anomalies Induced by El Nino?

The El Nino Southern Oscillation is the dominant year-to-year mode of global climate variability. El Nino effects on terrestrial carbon cycling are mediated by associated climate anomalies, primarily drought, influencing fire emissions and biotic net ecosystem exchange (NEE). Here we evaluate whether El Nino produces a consistent response from the global carbon cycle. We apply a novel bottom-up approach to estimating global NEE anomalies based on FLUXNET data using land cover maps and weather reanalysis. We analyze 13 years (1997-2009) of globally gridded observational NEE anomalies derived from eddy covariance flux data, remotely-sensed fire emissions at the monthly time step, and NEE estimated from an atmospheric transport inversion. We evaluate the overall consistency of biospheric response to El Nino and, more generally, the link between global CO2 flux anomalies and El Nino-induced drought. Our findings, which are robust relative to uncertainty in both methods and time-lags in response, indicate that each event has a different spatial signature with only limited spatial coherence in Amazonia, Australia and southern Africa. For most regions, the sign of response changed across El Nino events. Biotic NEE anomalies, across 5 El Nino events, ranged from -1.34 to +0.98 Pg Cyr(exp -1, whereas fire emissions anomalies were generally smaller in magnitude (ranging from -0.49 to +0.53 Pg C yr(exp -1). Overall drought does not appear to impose consistent terrestrial CO2 flux anomalies during El Ninos, finding large variation in globally integrated responses from 11.15 to +0.49 Pg Cyr(exp -1). Despite the significant correlation between the CO2 flux and El Nino indices, we find that El Nino events have, when globally integrated, both enhanced and weakened terrestrial sink strength, with no consistent response across event

Continental-Scale Partitioning of Fire Emissions During the 1997 to 2001 El Niño/La Niña Period

During the 1997 to 1998 El Niño, drought conditions triggered widespread increases in fire activity, releasing CH_4 and CO_2 to the atmosphere. We evaluated the contribution of fires from different continents to variability in these greenhouse gases from 1997 to 2001, using satellite-based estimates of fire activity, biogeochemical modeling, and an inverse analysis of atmospheric CO anomalies. During the 1997 to 1998 El Niño, the fire emissions anomaly was 2.1 ± 0.8 petagrams of carbon, or 66 ± 24% of the CO_2 growth rate anomaly. The main contributors were Southeast Asia (60%), Central and South America (30%), and boreal regions of Eurasia and North America (10%)

Sensitivity of CO2 Simulation in a GCM to the Convective Transport Algorithms

Convection plays an important role in the transport of heat, moisture and trace gases. In this study, we simulated CO2 concentrations with an atmospheric general circulation model (GCM). Three different convective transport algorithms were used. One is a modified Arakawa-Shubert scheme that was native to the GCM; two others used in two off-line chemical transport models (CTMs) were added to the GCM here for comparison purposes. Advanced CO2 surfaced fluxes were used for the simulations. The results were compared to a large quantity of CO2 observation data. We find that the simulation results are sensitive to the convective transport algorithms. Overall, the three simulations are quite realistic and similar to each other in the remote marine regions, but are significantly different in some land regions with strong fluxes such as Amazon and Siberia during the convection seasons. Large biases against CO2 measurements are found in these regions in the control run, which uses the original GCM. The simulation with the simple diffusive algorithm is better. The difference of the two simulations is related to the very different convective transport speed

The rp-process and new measurements of beta-delayed proton decay of light Ag and Cd isotopes

Recent network calculations suggest that a high temperature rp-process could explain the abundances of light Mo and Ru isotopes, which have long challenged models of p-process nuclide production. Important ingredients to network calculations involving unstable nuclei near and at the proton drip line are $\beta$-halflives and decay modes, i.e., whether or not $\beta$-delayed proton decay takes place. Of particular importance to these network calculation are the proton-rich isotopes $^{96}$Ag, $^{98}$Ag, $^{96}$Cd and $^{98}$Cd. We report on recent measurements of $\beta$-delayed proton branching ratios for $^{96}$Ag, $^{98}$Ag, and $^{98}$Cd at the on-line mass separator at GSI.Comment: 4 pages, uses espcrc1.sty. Proceedings of the 4th International Symposium Nuclei in the Cosmos, June 1996, Notre Dame/IN, USA, Ed. M. Wiescher, to be published in Nucl.Phys.A. Also available at ftp://ftp.physics.ohio-state.edu/pub/nucex/nic96-gs

Comparing Global Atmospheric CO2 Flux and Transport Models with Remote Sensing (and Other) Observations

We report recent progress derived from comparison of global CO2 flux and transport models with new remote sensing and other sources of CO2 data including those from satellite. The overall objective of this activity is to improve the process models that represent our understanding of the workings of the atmospheric carbon cycle. Model estimates of CO2 surface flux and atmospheric transport processes are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, to provide the basic framework for carbon data assimilation, and ultimately for future projections of carbon-climate interactions. Models can also be used to test consistency within and between CO2 data sets under varying geophysical states. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 2000 through 2009. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at 1x1 degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-3), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to remote sensing observations from TCCON, GOSAT, and AIRS as well as relevant in situ observations. Examples of the influence of key process representations are shown from both forward and inverse model comparisons. We find that the model can resolve much of the synoptic, seasonal, and interannual variability in the observations, although reasons for persistent discrepancies in northern hemisphere vegetation uptake are examined. At this time, we do not find any serious shortcomings in the model transport representation, but this is still the subject of close scrutiny. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in CO2 fluxes and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time

Deciphering the components of regional net ecosystem fluxes following a bottom-up approach for the Iberian Peninsula

Quantification of ecosystem carbon pools is a fundamental requirement for estimating carbon fluxes and for addressing the dynamics and responses of the terrestrial carbon cycle to environmental drivers. The initial estimates of carbon pools in terrestrial carbon cycle models often rely on the ecosystem steady state assumption, leading to initial equilibrium conditions. In this study, we investigate how trends and inter-annual variability of net ecosystem fluxes are affected by initial non-steady state conditions. Further, we examine how modeled ecosystem responses induced exclusively by the model drivers can be separated from the initial conditions. For this, the Carnegie-Ames-Stanford Approach (CASA) model is optimized at set of European eddy covariance sites, which support the parameterization of regional simulations of ecosystem fluxes for the Iberian Peninsula, between 1982 and 2006. <br><br> The presented analysis stands on a credible model performance for a set of sites, that represent generally well the plant functional types and selected descriptors of climate and phenology present in the Iberian region – except for a limited Northwestern area. The effects of initial conditions on inter-annual variability and on trends, results mostly from the recovery of pools to equilibrium conditions; which control most of the inter-annual variability (IAV) and both the magnitude and sign of most of the trends. However, by removing the time series of pure model recovery from the time series of the overall fluxes, we are able to retrieve estimates of inter-annual variability and trends in net ecosystem fluxes that are quasi-independent from the initial conditions. This approach reduced the sensitivity of the net fluxes to initial conditions from 47% and 174% to −3% and 7%, for strong initial sink and source conditions, respectively. <br><br> With the aim to identify and improve understanding of the component fluxes that drive the observed trends, the net ecosystem production (NEP) trends are decomposed into net primary production (NPP) and heterotrophic respiration (<i>R</i><sub>H</sub>) trends. The majority (~97%) of the positive trends in NEP is observed in regions where both NPP and <i>R</i><sub>H</sub> fluxes show significant increases, although the magnitude of NPP trends is higher. Analogously, ~83% of the negative trends in NEP are also associated with negative trends in NPP. The spatial patterns of NPP trends are mainly explained by the trends in <i>f</i>APAR (<i>r</i>=0.79) and are only marginally explained by trends in temperature and water stress scalars (<i>r</i>=0.10 and <i>r</i>=0.25, respectively). Further, we observe the significant role of substrate availability (<i>r</i>=0.25) and temperature (<i>r</i>=0.23) in explaining the spatial patterns of trends in <i>R</i><sub>H</sub>. These results highlight the role of primary production in driving ecosystem fluxes. <br><br> Overall, our study illustrates an approach for removing the confounding effects of initial conditions and emphasizes the need to decompose the ecosystem fluxes into its components and drivers for more mechanistic interpretations of modeling results. We expect that our results are not only specific for the CASA model since it incorporates concepts of ecosystem functioning and modeling assumptions common to biogeochemical models. A direct implication of these results is the ability of this approach to detect climate and phenology induced trends regardless of the initial conditions

A Real-Space Full Multigrid study of the fragmentation of Li11+ clusters

We have studied the fragmentation of Li11+ clusters into the two experimentally observed products (Li9+,Li2) and (Li10+,Li) The ground state structures for the two fragmentation channels are found by Molecular Dynamics Simulated Annealing in the framework of Local Density Functional theory. Energetics considerations suggest that the fragmentation process is dominated by non-equilibrium processes. We use a real-space approach to solve the Kohn-Sham problem, where the Laplacian operator is discretized according to the Mehrstellen scheme, and take advantage of a Full MultiGrid (FMG) strategy to accelerate convergence. When applied to isolated clusters we find our FMG method to be more efficient than state-of-the-art plane wave calculations.Comment: 9 pages + 6 Figures (in gzipped tar file

Quantifying the Observability of CO2 Flux Uncertainty in Atmospheric CO2 Records Using Products from Nasa's Carbon Monitoring Flux Pilot Project

NASAs Carbon Monitoring System (CMS) Flux Pilot Project (FPP) was designed to better understand contemporary carbon fluxes by bringing together state-of-the art models with remote sensing datasets. Here we report on simulations using NASAs Goddard Earth Observing System Model, version 5 (GEOS-5) which was used to evaluate the consistency of two different sets of observationally constrained land and ocean fluxes with atmospheric CO2 records. Despite the strong data constraint, the average difference in annual terrestrial biosphere flux between the two land (NASA Ames CASA and CASA-GFED) models is 1.7 Pg C for 2009-2010. Ocean models (NOBM and ECCO2-Darwin) differ by 35 in their global estimates of carbon flux with particularly strong disagreement in high latitudes. Based upon combinations of terrestrial and ocean fluxes, GEOS-5 reasonably simulated the seasonal cycle observed at northern hemisphere surface sites and by the Greenhouse gases Observing SATellite (GOSAT) while the model struggled to simulate the seasonal cycle at southern hemisphere surface locations. Though GEOS-5 was able to reasonably reproduce the patterns of XCO2 observed by GOSAT, it struggled to reproduce these aspects of AIRS observations. Despite large differences between land and ocean flux estimates, resulting differences in atmospheric mixing ratio were small, typically less than 5 ppmv at the surface and 3 ppmv in the XCO2 column. A statistical analysis based on the variability of observations shows that flux differences of these magnitudes are difficult to distinguish from natural variability, regardless of measurement platform