Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

Estimates of CO2 fluxes that are based on atmospheric measurements rely upon a meteorology model to simulate atmospheric transport. These models provide a quantitative link between the surface fluxes and CO2 measurements taken downwind. Errors in the meteorology can therefore cause errors in the estimated CO2 fluxes. Meteorology errors that correlate or covary across time and/or space are particularly worrisome; they can cause biases in modeled atmospheric CO2 that are easily confused with the CO2 signal from surface fluxes, and they are difficult to characterize. In this paper, we leverage an ensemble of global meteorology model outputs combined with a data assimilation system to estimate these biases in modeled atmospheric CO2. In one case study, we estimate the magnitude of month-long CO2 biases relative to CO2 boundary layer enhancements and quantify how that answer changes if we either include or remove error correlations or covariances. In a second case study, we investigate which meteorological conditions are associated with these CO2 biases.

In the first case study, we estimate uncertainties of 0.5-7 ppm in monthly-averaged CO2 concentrations, depending upon location (95% confidence interval). These uncertainties correspond to 13-150% of the mean afternoon CO2 boundary layer enhancement at individual observation sites. When we remove error covariances, however, this range drops to 2-22%. Top-down studies that ignore these covariances could therefore underestimate the uncertainties and/or propagate transport errors into the flux estimate.

In the second case study, we find that these month-long errors in atmospheric transport are anti-correlated with temperature and planetary boundary layer (PBL) height over terrestrial regions. In marine environments, by contrast, these errors are more strongly associated with weak zonal winds. Many errors, however, are not correlated with a single meteorological parameter, suggesting that a single meteorological proxy is not sufficient to characterize uncertainties in atmospheric CO2. Together, these two case studies provide information to improve the setup of future top-down inverse modeling studies, preventing unforeseen biases in estimated CO2 fluxes.

BACKGROUND: As next generation sequencing for the genetic diagnosis of cardiovascular disorders becomes more widely used, establishing causality for putative disease causing variants becomes increasingly relevant. Diseases of the cardiac sarcomere provide

We analyze the North American budget for carbon monoxide using data for CO and formaldehyde concentrations from tall towers and aircraft in a model-data assimilation framework. The Stochastic Time-Inverted Lagrangian Transport model for CO (STILT-CO) determines local to regional-scale CO contributions associated with production from fossil fuel combustion, biomass burning, and oxidation of volatile organic compounds (VOCs) using an ensemble of Lagrangian particles driven by high resolution assimilated meteorology. In many cases, the model demonstrates high fidelity simulations of hourly surface data from tall towers and point measurements from aircraft, with somewhat less satisfactory performance in coastal regions and when CO from large biomass fires in Alaska and the Yukon Territory influence the continental US. Inversions of STILT-CO simulations for CO and formaldehyde show that current inventories of CO emissions from fossil fuel combustion are significantly too high, by almost a factor of three in summer and a factor two in early spring, consistent with recent analyses of data from the INTEX-A aircraft program. Formaldehyde data help to show that sources of CO from oxidation of CH4 and other VOCs represent the dominant sources of CO over North America in summer. © Author(s) 2008.

Abstract. We analyze the North American budget for carbon monoxide using data for CO and formaldehyde concentrations from tall towers and aircraft in a model-data assimilation framework. The Stochastic Time-Inverted, Lagrangian Transport model for CO (STILT-CO) determines local to regional-scale CO contributions associated with production from fossil fuel combustion, biomass burning, and oxidation of volatile organic compounds (VOCs) using an ensemble of Lagrangian particles driven by high resolution assimilated meteorology. In most cases, the model demonstrates high fidelity simulations of hourly surface data from tall towers and point measurements from aircraft, with somewhat less satisfactory performance in coastal regions and when CO from large biomass fires in Alaska and the Yukon Territory influence the continental US. Inversions of STILT-CO simulations for CO and formaldehyde show that current inventories of CO emissions from fossil fuel combustion are significantly too high, by almost a factor of three in summer and a factor two in early spring, consistent with recent analyses of data from the INTEX-A aircraft program. Formaldehyde data help to show that sources of CO from oxidation of CH4 and other VOCs represent the dominant sources of CO over North America in summer.

© 2014 American Chemical Society.Mercuric reductase, MerA, is a key enzyme in bacterial mercury resistance. This homodimeric enzyme captures and reduces toxic Hg2+ to Hg0, which is relatively unreactive and can exit the cell passively. Prior to reduction,