[1] We introduce a tool to determine surface fluxes from atmospheric concentration data in the midst of distributed sources or sinks over land, the Stochastic Time-Inverted Lagrangian Transport (STILT) model, and illustrate the use of the tool with CO 2 data over North America. Anthropogenic and biogenic emissions of trace gases at the surface cause large variations of atmospheric concentrations in the planetary boundary layer (PBL) from the ''near field,'' where upstream sources and sinks have strong influence on observations. Transport in the near field often takes place on scales not resolved by typical grid sizes in transport models. STILT provides the capability to represent near-field influences, transforming this noise to signal useful in diagnosing surface emissions. The model simulates transport by following the time evolution of a particle ensemble, interpolating meteorological fields to the subgrid scale location of each particle. Turbulent motions are represented by a Markov chain process. Significant computational savings are realized because the influence of upstream emissions at different times is modeled using a single particle simulation backward in time, starting at the receptor and sampling only the portion of the domain that influences the observations. We assess in detail the physical and numerical requirements of STILT and other particle models necessary to avoid inconsistencies and to preserve time symmetry (reversibility). We show that source regions derived from backward and forward time simulations in STILT are similar, and we show that deviations may be attributed to violation of mass conservation in currently available analyzed meterological fields. Using concepts from information theory, we show that the particle approach can provide significant gains in information compared to conventional gridcell models, principally during the first hours of transport backward in time, when PBL observations are strongly affected by surface sources and sinks.

Abstract. The Total Carbon Column Observing Network (TCCON) produces precise measurements of the column average dry-air mole fractions of CO 2 , CO, CH 4 , N 2 O and H 2 O at a variety of sites worldwide. These observations rely on spectroscopic parameters that are not known with sufficient accuracy to compute total columns that can be used in combination with in situ measurements. The TCCON must therefore be calibrated to World Meteorological Orga-

[1] The causes of renewed growth in the atmospheric CH 4 burden since 2007 are still poorly understood and subject of intensive scientific discussion. We present a reanalysis of global CH 4 emissions during the 2000s, based on the TM5-4DVAR inverse modeling system. The model is optimized using high-accuracy surface observations from NOAA ESRL's global air sampling network for 2000-2010 combined with retrievals of column-averaged CH 4 mole fractions from SCIAMACHY onboard ENVISAT (starting 2003). Using climatological OH fields, derived global total emissions for 2007-2010 are 16-20 Tg CH 4 /yr higher compared to [2003][2004][2005]. Most of the inferred emission increase was located in the tropics (9-14 Tg CH 4 /yr) and mid-latitudes of the northern hemisphere (6-8 Tg CH 4 /yr), while no significant trend was derived for Arctic latitudes. The atmospheric increase can be attributed mainly to increased anthropogenic emissions, but the derived trend is significantly smaller than estimated in the EDGARv4.2 emission inventory. Superimposed on the increasing trend in anthropogenic CH 4 emissions are significant inter-annual variations (IAV) of emissions from wetlands (up to AE10 Tg CH 4 /yr), and biomass burning (up to AE7 Tg CH 4 /yr). Sensitivity experiments, which investigated the impact of the SCIAMACHY observations (versus inversions using only surface observations), of the OH fields used, and of a priori emission inventories, resulted in differences in the detailed latitudinal attribution of CH 4 emissions, but the IAV and trends aggregated over larger latitude bands were reasonably robust. All sensitivity experiments show similar performance against independent shipboard and airborne observations used for validation, except over Amazonia where satellite retrievals improved agreement with observations in the free troposphere. Citation: Bergamaschi, P., et al. (2013), Atmospheric CH 4 in the first decade of the 21st century: Inverse modeling analysis using SCIAMACHY satellite retrievals and NOAA surface measurements,

We present the Vegetation Photosynthesis and Respiration Model (VPRM), a satellite‐based assimilation scheme that estimates hourly values of Net Ecosystem Exchange (NEE) of CO2 for 12 North American biomes using the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI), derived from reflectance data of the Moderate Resolution Imaging Spectroradiometer (MODIS), plus high‐resolution data for sunlight and air temperature. The motivation is to provide reliable, fine‐grained first‐guess fields of surface CO2 fluxes for application in inverse models at continental and smaller scales. An extremely simple mathematical structure, with minimal numbers of parameters, facilitates optimization using in situ data, with finesse provided by maximal infusion of observed NEE and environmental data from networks of eddy covariance towers across North America (AmeriFlux and Fluxnet Canada). Cross validation showed that the VPRM has strong prediction ability for hourly to monthly timescales for sites with similar vegetation. The VPRM also provides consistent partitioning of NEE into Gross Ecosystem Exchange (GEE, the light‐dependent part of NEE) and ecosystem respiration (R, the light‐independent part), half‐saturation irradiance of ecosystem photosynthesis, and annual sum of NEE at all eddy flux sites for which it is optimized. The capability to provide reliable patterns of surface flux for fine‐scale inversions is presently limited by the number of vegetation classes for which NEE can be constrained by the current network of eddy flux sites and by the accuracy of MODIS data and data for sunlight.