[1] Properly handling satellite data to constrain the inversion of CO 2 sources and sinks at the Earth surface is a challenge motivated by the limitations of the current surface observation network. In this paper we present a Bayesian inference scheme to tackle this issue. It is based on the same theoretical principles as most inversions of the flask network but uses a variational formulation rather than a pure matrix-based one in order to cope with the large amount of satellite data. The minimization algorithm iteratively computes the optimum solution to the inference problem as well as an estimation of its error characteristics and some quantitative measures of the observation information content. A global climate model, guided by analyzed winds, provides information about the atmospheric transport to the inversion scheme. A surface flux climatology regularizes the inference problem. This new system has been applied to 1 year's worth of retrievals of vertically integrated CO 2 concentrations from the Television Infrared Observation Satellite Operational Vertical Sounder (TOVS). Consistent with a recent study that identified regional biases in the TOVS retrievals, the inferred fluxes are not useful for biogeochemical analyses. In addition to the detrimental impact of these biases, we find a sensitivity of the results to the formulation of the prior uncertainty and to the accuracy of the transport model. Notwithstanding these difficulties, four-dimensional inversion schemes of the type presented here could form the basis of multisensor data assimilation systems for the estimation of the surface fluxes of key atmospheric compounds.Citation: Chevallier, F., M. Fisher, P. Peylin, S. Serrar, P. Bousquet, F.-M. Bréon, A. Chédin, and P. Ciais (2005), Inferring CO 2 sources and sinks from satellite observations: Method and application to TOVS data,

[1] This paper presents the aerosol modeling now part of the ECMWF Integrated Forecasting System (IFS). It includes new prognostic variables for the mass of sea salt, dust, organic matter and black carbon, and sulphate aerosols, interactive with both the dynamics and the physics of the model. It details the various parameterizations used in the IFS to account for the presence of tropospheric aerosols. Details are given of the various formulations and data sets for the sources of the different aerosols and of the parameterizations describing their sinks. Comparisons of monthly mean and daily aerosol quantities like optical depths against satellite and surface observations are presented. The capability of the forecast model to simulate aerosol events is illustrated through comparisons of dust plume events. The ECMWF IFS provides a good description of the horizontal distribution and temporal variability of the main aerosol types. The forecastonly model described here generally gives the total aerosol optical depth within 0.12 of the relevant observations and can therefore provide the background trajectory information for the aerosol assimilation system described in part 2 of this paper.

[1] The ability to reliably estimate CO 2 fluxes from current in situ atmospheric CO 2 measurements and future satellite CO 2 measurements is dependent on transport model performance at synoptic and shorter timescales. The TransCom continuous experiment was designed to evaluate the performance of forward transport model simulations at hourly, daily, and synoptic timescales, and we focus on the latter two in this paper. Twenty-five transport models or model variants submitted hourly time series of nine predetermined tracers (seven for CO 2 ) at 280 locations. We extracted synoptic-scale variability from daily averaged CO 2 time series using a digital filter and analyzed the results by comparing them to atmospheric measurements at 35 locations. The correlations between modeled and observed synoptic CO 2 variabilities were almost always largest with zero time lag and statistically significant for most models and most locations. Generally, the model results using diurnally varying land fluxes were closer to the observations compared to those obtained using monthly mean or daily average fluxes, and winter was often better simulated than summer. Model results at higher spatial resolution compared better with observations, mostly because these models were able to sample closer to the measurement site location. The amplitude and correlation of model-data variability is strongly model and season dependent. Overall similarity in modeled synoptic CO 2 variability suggests that the first-order transport mechanisms are fairly well parameterized in the models, and no clear distinction was found between the meteorological analyses in capturing the synoptic-scale dynamics.

[1] A forward atmospheric transport modeling experiment has been coordinated by the TransCom group to investigate synoptic and diurnal variations in CO 2 . Model simulations were run for biospheric, fossil, and air-sea exchange of CO 2 and for SF 6 and radon for [2000][2001][2002][2003]. Twenty-five models or model variants participated in the comparison. Hourly concentration time series were submitted for 280 sites along with vertical profiles, fluxes, and meteorological variables at 100 sites. The submitted results have been analyzed for diurnal variations and are compared with observed CO 2 in 2002. Mean summer diurnal cycles vary widely in amplitude across models. The choice of sampling location and model level account for part of the spread suggesting that representation errors in these types of models are potentially large. Despite the model spread, most models simulate the relative variation in diurnal amplitude between sites reasonably well. The modeled diurnal amplitude only shows a weak relationship with vertical resolution across models; differences in near-surface transport simulation appear to play a major role. Examples are also presented where there is evidence that the models show useful skill in simulating seasonal and synoptic changes in diurnal amplitude.

Abstract. This study presents a synthetic model intercomparison to investigate the importance of transport model errors for estimating the sources and sinks of CO 2 using satellite measurements. The experiments were designed for testing the potential performance of the proposed CO 2 lidar A-SCOPE, but also apply to other space borne missions that monitor total column CO 2 . The participating transport models IFS, LMDZ, TM3, and TM5 were run in forward and inverse mode using common a priori CO 2 fluxes and initial concentrations. Forward simulations of column averaged CO 2 (xCO 2 ) mixing ratios vary between the models by σ =0.5 ppm over the continents and σ =0.27 ppm over the oceans. Despite the fact that the models agree on average on the sub-ppm level, these modest differences nevertheless lead to significant discrepancies in the inverted fluxes of 0.1 PgC/yr per 10 6 km 2 over land and 0.03 PgC/yr per 10 6 km 2 over the ocean. These transport model induced flux uncertainties exceed the target requirement that was formulated for the A-SCOPE mission of 0.02 PgC/yr per 10 6 km 2 , and could also limit the overall performance of other CO 2 missions such as GOSAT. A variable, but overall encouraging agreement is found in comparison with FTS Correspondence to: S. Houweling (s.houweling@sron.nl) measurements at Park Falls, Darwin, Spitsbergen, and Bremen, although systematic differences are found exceeding the 0.5 ppm level. Because of this, our estimate of the impact of transport model uncerainty is likely to be conservative. It is concluded that to make use of the remote sensing technique for quantifying the sources and sinks of CO 2 not only requires highly accurate satellite instruments, but also puts stringent requirements on the performance of atmospheric transport models. Improving the accuracy of these models should receive high priority, which calls for a closer collaboration between experts in atmospheric dynamics and tracer transport.

[1] Midtropospheric mean atmospheric CO 2 concentration is retrieved from the observations of the NOAA series of polar meteorological satellites, using a nonlinear regression inference scheme. For the 4 years of the present analysis (July 1987 to June 1991, monthly means of the CO 2 concentration retrieved over the tropics (20°N to 20°S) from NOAA 10 show very good agreement with what is presently known. Not only the phase of the seasonal variations (location of the peaks) but also their amplitude and their latitudinal evolution match quite well recent in situ observations made by properly equipped commercial airliners measuring in an altitude range similar to the one favored by the satellite observations. Moreover, the annual trend inferred corresponds to the known increase in the concentration of CO 2 as a result of human activities. Also, the impact of El Niño-Southern Oscillation events is clearly seen and confirms analyses of in situ or aircraft observations and of model simulations. Forty-eight maps of monthly mean midtropospheric CO 2 concentration have been produced at a resolution of 15°Â 15°. A rough estimate of the method-induced standard deviation of these retrievals is of the order of 3.6 ppmv (around 1%). The coming analysis of the almost 25 years of archive already accumulated by the NOAA platforms should contribute to a better understanding of the carbon cycle. A simulation of the extension of the method to the next generation high-spectral-resolution instruments, with very encouraging results, is presented.

[1] The European Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in situ data (GEMS) project has built a system that is capable of assimilating various sources of satellite and in situ observations to monitor the atmospheric concentrations of CO 2 and its surface fluxes. This consists of an atmospheric four-dimensional variational data assimilation system that provides atmospheric fields to a separate variational flux inversion scheme. In this paper, we describe the atmospheric data assimilation system that currently uses radiance observations from the Atmospheric Infrared Sounder (AIRS) to constrain the CO 2 mixing ratios of the data assimilation model. We present the CO 2 transport model, the bias correction of the observation-model mismatch, and the estimation of the background error covariance matrix. Data assimilation results are compared to independent CO 2 observations from NOAA/ESRL aircraft showing a reduction of the mean difference of up to 50% depending on the altitude of the aircraft observations relative to an unconstrained transport model simulation. In the coming years, observations from dedicated CO 2 satellite missions will be added to the system. Together with improved error characterization and bias correction, we hope to show that satellite observations can indeed complement the in situ observation system to get a better estimate of global carbon fluxes.

Eight years of cloud properties retrieved from Television Infrared Observation Satellite-N (TIROS-N) Observational Vertical Sounder (TOVS) observations aboard the NOAA polar orbiting satellites are presented. The relatively high spectral resolution of these instruments in the infrared allows especially reliable cirrus identification day and night. This dataset therefore provides complementary information to the International Satellite Cloud Climatology Project (ISCCP). According to this dataset, cirrus clouds cover about 27% of the earth and 45% of the Tropics, whereas ISCCP reports 19% and 25%, respectively. Both global datasets agree within 5% on the amount of single-layer low clouds, at 30%. From 1987 to 1995, global cloud amounts remained stable to within 2%. The seasonal cycle of cloud amount is in general stronger than its diurnal cycle and it is stronger than the one of effective cloud amount, the latter the relevant variable for radiative transfer. Maximum effective low cloud amount over ocean occurs in winter in SH subtropics in the early morning hours and in NH midlatitudes without diurnal cycle. Over land in winter the maximum is in the early afternoon, accompanied in the midlatitudes by thin cirrus. Over tropical land and in the other regions in summer, the maximum of mesoscale high opaque clouds occurs in the evening. Cirrus also increases during the afternoon and persists during night and early morning. The maximum of thin cirrus is in the early afternoon, then decreases slowly while cirrus and high opaque clouds increase. TOVS extends information of ISCCP during night, indicating that high cloudiness, increasing during the afternoon, persists longer during night in the Tropics and subtropics than in midlatitudes. A comparison of seasonal and diurnal cycle of high cloud amount between South America, Africa, and Indonesia during boreal winter has shown strong similarities between the two land regions, whereas the Indonesian islands show a seasonal and diurnal behavior strongly influenced by the surrounding ocean. Deeper precipitation systems over Africa than over South America do not seem to be directly reflected in the horizontal coverage and mesoscale effective emissivity of high clouds.