Sensitivity of biomass burning emissions estimates to land surface information

Emissions from biomass burning (BB) are a key source of atmospheric tracer gases that affect the atmospheric carbon cycle. We estimated four types of global BB emissions using a bottom-up approach and by combining the remote sensing products related to fire distribution with two aboveground biomass (AGB) and two land cover classification (LCC) distributions. The sensitivity of the estimates of BB emissions to the AGB and LCC data was evaluated using the carbon monoxide (CO) emissions associated with each BB estimate. We found a substantial spatial difference in CO emissions for both the AGB and LCC data, which resulted in a large (factor of approximately three) spread of estimates for the mean annual CO emissions. We simulated atmospheric CO variability using an atmospheric tracer transport model and the BB emissions estimates and compared it with ground-based and satellite observations. At ground-based observation sites during fire seasons, statistical comparisons indicated that the impact of differences in the BB emissions estimates on atmospheric CO variability was poorly defined in our simulations. However, when compared at the regional and global scales, the distribution of atmospheric CO concentrations in the simulations show substantial differences among the estimates of BB emissions. These results indicate that the estimates of BB emissions are highly sensitive to the AGB and LCC data.

A Marginal Adjustment Rank Histogram Filter for Non-Gaussian Ensemble Data Assimilation

An extension to standard ensemble Kalman filter algorithms that can improve performance for non-Gaussian prior distributions, non-Gaussian likelihoods, and bounded state variables is described. The algorithm exploits the capability of the rank histogram filter (RHF) to represent arbitrary prior distributions for observed variables. The rank histogram algorithm can be applied directly to state variables to produce posterior marginal ensembles without the need for regression that is part of standard ensemble filters. These marginals are used to adjust the marginals obtained from a standard ensemble filter that uses regression to update state variables. The final posterior ensemble is obtained by doing an ordered replacement of the posterior marginal ensemble values from a standard ensemble filter with the values obtained from the rank histogram method applied directly to state variables; the algorithm is referred to as the Marginal Adjustment Rank Histogram Filter (MARHF). Applications to idealized bivariate problems and low-order dynamical systems show that the MARHF can produce better results than standard ensemble methods for priors that are non-Gaussian. Like the original RHF, the MARHF can also make use of arbitrary non-Gaussian observation likelihoods. The MARHF also has advantages for problems with bounded state variables, for instance the concentration of an atmospheric tracer. Bounds can be automatically respected in the posterior ensembles. With an efficient implementation of the MARHF, the additional cost has better scaling than the standard RHF.

Data assimilation using an ensemble of models: a hierarchical approach

One characteristic of biogeochemical models is uncertainty about their formulation. Data assimilation should take this uncertainty into account. A common approach is to use an ensemble of models. We must assign probabilities not only to the parameters of the models but also to the models themselves. The method of hierarchical modelling allows us to calculate these probabilities. This paper describes the approach, develops the algebra for the most common case and then applies it to the Atmospheric Tracer Transport Model Intercomparison Project (TransCom). We see that the discrimination among models is unrealistically strong, due to optimistic assumptions inherent in the underlying inversion. The weighted ensemble means and variances from the hierarchical approach are quite similar to the conventional values because the best model in the ensemble is also quite close to the ensemble mean. The approach can also be used for cross-validation in which some data are held back to test estimates obtained with the rest. We demonstrate this with a test of the TransCom inversions holding back the airborne data. We see a slight decrease in the tropical sink and a notably different preferred order of models.

Three-dimensional simulation of stratospheric gravitational separation using the NIES global atmospheric tracer transport model

A three-dimensional simulation of gravitational separation, defined as the process of atmospheric molecule separation under gravity according to their molar masses, is performed for the first time in the upper troposphere and lower stratosphere. We analyze distributions of two isotopes with a small difference in molecular mass (13C16O2 (Mi=45) and 12C16O2 (Mi=44)) simulated by the National Institute for Environmental Studies (NIES) chemical transport model (TM) with a parameterization of molecular diffusion. The NIES model employs global reanalysis and an isentropic vertical coordinate and uses optimized CO2 fluxes. The applicability of the NIES TM to the modeling of gravitational separation is demonstrated by a comparison with measurements recorded by high-precision cryogenic balloon-borne samplers in the lower stratosphere. We investigate the processes affecting the seasonality of gravitational separation and examine the age of air derived from the tracer distributions modeled by the NIES TM. We find a strong relationship between age of air and gravitational separation for the main climatic zones. The advantages and limitations of using age of air and gravitational separation as indicators of the variability in the stratosphere circulation are discussed.

Three-dimensional simulation of stratospheric gravitational separation using the NIES global atmospheric tracer transport model

A three-dimensional simulation of gravitational separation, defined as the process of atmospheric molecule separation under gravity according to their molar masses, is performed for the first time in the upper troposphere and lower stratosphere. We analyze distributions of two isotopes with a small difference in molecular mass (13C16O2 (Mi = 45) and 12C16O2 (Mi = 44)) simulated by the National Institute for Environmental Studies (NIES) chemical transport model with a parameterization of molecular diffusion. The NIES model employs global reanalysis and an isentropic vertical coordinate and uses optimized CO2 fluxes. This study includes a comparison with measurements recorded by cryogenic balloon-borne samplers in the lower stratosphere and two-dimensional model simulations. The benefits of the NIES TM simulations are discussed. We investigate the processes affecting gravitational separation by a detailed estimation of terms in the molecular diffusion equation. At the same time, we study the age of air derived from the tracer distributions. We find a strong relationship between age of air and gravitational separation for the main climatic zones. The advantages and limitations of using age of air and gravitational separation as indicators of the variability in the stratosphere circulation are discussed.

Correcting atmospheric CO₂ and CH₄ mole fractions obtained with Picarro analyzers for sensitivity of cavity pressure to water vapor

Measurements of dry air mole fractions of atmospheric greenhouse gases are used in inverse models of atmospheric tracer transport to quantify their sources and sinks. The measurements have to be calibrated to a common scale to avoid bias in the inferred fluxes. For this purpose, the World Meteorological Organization (WMO) has set requirements for the inter-laboratory compatibility of atmospheric greenhouse gas (GHG) measurements. A widely used series of devices for these measurements are the GHG analyzers manufactured by Picarro, Inc. These are often operated in humid air, and the effects of water vapor are corrected for in post-processing. Here, we report on rarely detected and previously unexplained biases of the water correction method for CO2 and CH4 in the literature. They are largest at water vapor mole fractions below 0.5 % H2O, which were undersampled in previous studies, and can therefore affect measurements obtained in humid air and in air dried with a Nafion membrane. The biases, which can amount to considerable fractions of the WMO goals, are caused by a sensitivity of the pressure in the measurement cavity to water vapor. We correct these biases by modifying the water correction method from the literature. Our method relies on experiments that maintain stable water vapor levels to allow equilibration of cavity pressure. The commonly used droplet method does not fulfill this requirement. Correcting CO2 measurements proved challenging, presumably because of our humidification method. Open questions pertain to differences between analyzers and variability over time. Correcting the cavity pressure-related biases helps keeping the overall accuracy of measurements of dry air mole fractions of CO2 and CH4 obtained with Picarro GHG analyzers in humid and Nafion-dried air within the WMO goals.

Reviews and syntheses: Carbonyl sulfide as a multi-scale tracer for carbon and water cycles

For the past decade, observations of carbonyl sulfide (OCS or COS) have been investigated as a proxy for carbon uptake by plants. OCS is destroyed by enzymes that interact with CO2 during photosynthesis, namely carbonic anhydrase (CA) and RuBisCO, where CA is the more important one. The majority of sources of OCS to the atmosphere are geographically separated from this large plant sink, whereas the sources and sinks of CO2 are co-located in ecosystems. The drawdown of OCS can therefore be related to the uptake of CO2 without the added complication of co-located emissions comparable in magnitude. Here we review the state of our understanding of the global OCS cycle and its applications to ecosystem carbon cycle science. OCS uptake is correlated well to plant carbon uptake, especially at the regional scale. OCS can be used in conjunction with other independent measures of ecosystem function, like solar-induced fluorescence and carbon and water isotope studies. More work needs to be done to generate global coverage for OCS observations and to link this powerful atmospheric tracer to systems where fundamental questions concerning the carbon and water cycle remain.