Extreme atmospheric events are rare phenomena characterized by unusual intensity or chemical composition compared to the atmospheric background state. Many such events, in the context of global warming, pose threats to human health, thereby making their early detection and monitoring essential. The Infrared Atmospheric Sounding Interferometer (IASI), able to measure more than 30 atmospheric chemical species on a global scale, offers strong potential in this context. However, the large volume of current and upcoming satellite observations makes intelligent data screening more and more challenging. This work aims to overcome this limitation by developing a dedicated algorithm based on Principal Component Analysis (PCA) of Level 1C IASI spectra. Named IASI-PCA, it is designed for the systematic detection of fires, volcanic eruptions, dust storms, pollution plumes, and other extreme atmospheric events that remain unclassified. The detected events are defined as spectral outliers relative to the representative global variability of IASI observations under normal or unperturbed conditions. The detection of an individual spectrum is driven by its anomalous behavior in selected spectral domains, where a set of optimized indicators corresponding to more than 10 species has been defined to discriminate events according to their chemical signatures. This methodology has been implemented in a near-real-time operational system providing detection and classification products within one hour and one day, respectively. The IASI-PCA approach has been applied to multiple case studies demonstrating that it is a powerful tool for: (1) efficiently detecting and monitoring fires, whether isolated sources or extended plumes; (2) handling both clear and cloudy conditions; (3) detecting dust plumes predominantly associated with calcite; (4) identifying pollution sources; and (5) detecting volcanic events. The consistency of our results with those obtained with the EUMETSAT Principal Component Compression (PCC) is also illustrated through the processing of a representative study period.

Ammonia (NH3) is a short-lived atmospheric pollutant with significant environmental and health impacts. Monitoring NH3 remains challenging, as diurnal variability at local scales is still poorly documented. In this study, we analyze two years (July 2022-June 2024) of NH3 total columns from the Geostationary Interferometric Infrared Sounder onboard China's FengYun-4B (GIIRS-B) over East Asia. After applying quality and uncertainty filters, we find good agreement with Infrared Atmospheric Sounding Interferometer (IASI) morning observations (weighted Pearson R = 0.64) and identify relationships between NH3 and skin/land surface temperature over five major NH3 hotspots, suggesting contributions from agriculture (urea fertilizer use), livestock, and secondary urban sources. GIIRS-B's high temporal resolution reveals a clear bimodal diurnal pattern, with NH3 enhancements in the early morning and mid-afternoon in four regions. A dedicated analysis of GIIRS-B NH3 retrieval uncertainties provides a realistic physical benchmark for geostationary infrared observations. Using radiative transfer simulations (4A/OP) driven by atmospheric chemistry model outputs (CHIMERE), we evaluate the potential of the European InfraRed Sounder (IRS) onboard MTG-S to retrieve NH3 at sub-daily resolution. IRS uncertainties are generally larger and more variable than those of IASI, but under favorable thermal-contrast conditions they can become comparable. GIIRS-B and IRS exhibit consistent diurnal uncertainty patterns with nighttime maxima and daytime minima, confirming the realism of the IRS performance assessment. These results highlight the added value of geostationary sounders for improving NH3 emission monitoring, source attribution, and diurnal process understanding in support of future European air-quality regulations.

Ammonia (NH3) is an atmospheric pollutant mainly emitted by the agricultural sector, which has an effect on public health since it is a precursor of fine particles (PM2.5). The diurnal variability of NH3 in the atmosphere and its transformation into particles are poorly constrained and strongly depend on meteorological parameters, in particular temperature. This strongly influences our ability to correctly simulate NH3 emissions and associated particulate pollution events in atmospheric models The IRS (InfraRed Sounder) instrument which will be launched on the MTG (Meteosat Third Generation) satellite into geostationary orbit in late 2024, will offer the ability to evaluate NH3 diurnal variabilities and its dependence on atmospheric temperature with frequent measurements (every 30-45 minutes over Europe and Africa) and fine spatial resolution (4 km x 4 km at the Equator and Greenwich meridian). This work shows the potential of the European geostationary IRS-MTG mission to capture the spatio-temporal variability of ammonia and temperature focusing on a case study over the Brittany region in France. Synthetic spectra are simulated from the 4A/OP radiative transfer model using atmospheric states derived from the CHIMERE chemistry-transport model. The IRS NH3 observations are compared to the current IASI observations in terms of vertical sensitivity and error budget. The uncertainty analysis over the Brittany region is calculated using NH3 Jacobians computed from the 4A/OP radiative code and the noise covariance matrix provided by each satellite.

In this study, we carried out an independent validation of two methane retrieval algorithms using spectra from the Infrared Atmospheric Sounding Interferometer (IASI) that has been aboard the Meteorological Operational Satellite A (MetOp-A) since 2006. Both algorithms, one developed by the Laboratoire de M & eacute;t & eacute;orologie Dynamique (LMD), called the non-linear inference scheme (NLISv8.3), and the other by the Rutherford Appleton Laboratory (RAL), referred to as RALv2.0, provide long-term global CH4 concentrations using distinctively different retrieval approaches (neural network vs. optimal estimation, respectively). They also differ with respect to the vertical range covered, where LMD provides mid-tropospheric dry-air mole fractions (mtCH(4)), and RAL provides mixing ratio profiles from which we can derive total column-averaged dry-air mole fractions (XCH4) and potentially two partial column layers (qCH(4)). We compared both CH4 products using the Copernicus Atmospheric Monitoring Service (CAMS) model, in situ profiles (range extended using CAMS model data), and ground-based Fourier transform infrared (FTIR) remote-sensing measurements. The average difference (in mtCH(4)) with respect to in situ profiles for LMD ranges between -0.3 and 10.9 ppb, while for RAL the XCH(4 )difference ranges between -4.6 and -1.6 ppb. The standard deviation (SD) of the observed differences between in situ measurements and RAL retrievals is 14.1-21.9 ppb, which is consistently smaller than that between LMD retrievals and in situ measurements (15.2-30.6 ppb). By comparing with ground-based FTIR sites, the mean differences are within +/- 10 ppb for both RAL and LMD retrievals. However, the SD of the differences at the ground-based FTIR stations shows significantly lower values for RAL (11-15 ppb) than for LMD (about 25 ppb). The long-term trend and seasonal cycles of CH4 derived from the LMD and RAL products are further investigated and discussed. The seasonal variation in XCH4 derived from RAL is consistent with the seasonal variation observed by the ground-based FTIR measurements. However, the overall 2007-2015 XCH4 trend derived from RAL measurements is underestimated, if not adjusted, for an anomaly occurring on 16 May 2013 due to a L1 calibration change. For LMD, we see very good agreement at the (sub)tropics (<35 degrees N-35 degrees S) but notice deviations in the seasonal cycle (both in the amplitude and phase) and an underestimation of the long-term trend with respect to the RAL and reference data at higher-latitude sites.

Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

The three Infrared Atmospheric Sounding Interferometer (IASI) instruments on board the Metop family of satellites have been sounding the atmospheric composition since 2006. More than 30 atmospheric gases can be measured from the IASI radiance spectra, allowing the improvement of weather forecasting and the monitoring of atmospheric chemistry and climate variables. The early detection of extreme events such as fires, pollution episodes,volcanic eruptions, or industrial releases is key to take safety measures to protect the inhabitants and the environment in the impacted areas. With its near-real-time observations and good horizontal coverage, IASI can contribute to the series of monitoring systems for the systematic and continuous detection of exceptional atmospheric events in order to support operational decisions. In this paper, we describe a new approach to the near-real-time detectionand characterization of unexpected events, which relies on the principalcomponent analysis (PCA) of IASI radiance spectra. By analyzing both theIASI raw and compressed spectra, we applied a PCA-granule-based method onvarious past, well-documented extreme events such as volcanic eruptions,fires, anthropogenic pollution, and industrial accidents. We demonstratethat the method is well suited to the detection of spectral signatures for reactive and weakly absorbing gases, even for sporadic events. Consistent long-term records are also generated for fire and volcanic events from the available IASI/Metop-B data record. The method is running continuously, delivering email alerts on a routinebasis, using the near-real-time IASI L1C radiance data. It is planned to beused as an online tool for the early and automatic detection of extremeevents, which was not done before.

Ammonia (NH3) is an atmospheric pollutant mainly emitted by the agricultural sector. It is a precursor of fine particles (PM2.5) and therefore has a major effect on public health, and climate change. The volatilization process of NH3 and its lifetime in the atmosphere, as well as its transformation into particles, are poorly constrained and strongly depend on meteorological parameters, in particular temperature.Although current satellite measurements have evaluated NH3 spatio-temporal variabilities at various scales (global, regional, and local), observations of NH3 diurnal variability and their diurnal variability and dependence to temperature are poorly constrained. This strongly influences our ability to correctly simulate NH3 emissions and associated particulate pollution events in atmospheric models.The IRS (InfraRed Sounder) instrument which will be launched on the MTG (Meteosat Third Generation) satellite into geostationary orbit in late 2024, will offer the ability to deepen this analysis with more frequent measurements (every 30-45 minutes over Europe and Africa) and better spatially resolved observations (4 km x 4 km at the Equator).In this presentation, we show the potential of the new geostationary IRS-MTG mission to assess spatio-temporal variabilities of ammonia and temperature focusing on a case study over the high NH3 emitted region of Brittany (France). Using atmospheric states simulated using the CHIMERE chemistry-transport model at the effective spatial resolution of IRS over Brittany, synthetic spectra are computed using the 4A/OP radiative transfer model. NH3 measurement-sensitivity of the future IRS-MTG mission is discussed with regards to the presently available IASI observations.

Abstract Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

Power plants are a major source of CO2 globally. Although their emissions are routinely monitored in many countries especially in the developed world, these numbers are often not publicly available and a complete global record is still far from reality. An important goal of Europe's planned Copernicus CO2 satellite mission CO2M is therefore to provide an independent quantification of power plant emissions worldwide. Emissions may be estimated from satellite XCO2 observations by simulating the plumes with an atmospheric transport model and finding those emissions that minimize a cost function of the differences between simulation and observations. Here we present a comparison of CO2 plume simulations from six high-resolution models, three Large Eddy Simulation models, two mesoscale Eulerian models, and one Lagrangian particle dispersion model. Simulations were conducted for two large coal-fired power plants, Belchatow in Poland and Janschwalde in Germany, which were extensively observed with aircraft in situ and remote sensing measurements during the CoMet campaign in May-June 2018. The observations provide a unique opportunity to study the capability of the models to simulate such plumes in a realistic manner and to design optimal modelling and emission quantification strategies. The Belchatow plume was sampled under highly convective and turbulent conditions whereas the Janschwalde plume was observed in a more stable weather situation. The models are able to reproduce these differences by simulating a highly structured turbulent plume for Belchatow and a more Gaussian-shaped plume for Janschwalde. However, the models differ in many details including the horizontal and vertical spread of the plumes, suggesting that in addition to resolution the specific choices of turbulence and advection scheme have a significant impact on the results. Our findings suggest that estimating emissions from individual images is particularly challenging for turbulent plumes. Since turbulence intensity evolves with the build-up of the convective boundary layer, a satellite overpass well before noon would likely be an advantage.

Atmospheric methane is measured continuously from space, providing valuable information at global/regional scales for atmospheric monitoring as well as for surface flux estimates. However, as shown by several studies, CH4 atmospheric concentration retrievals from thermal infrared (TIR) nadir sensors exhibit significant biases compared to independent observations, or when intercompared between different TIR and SWIR/TIR sensors products. It is necessary to analyse the possible causes of biases and to investigate potential retrieval improvements and/or bias-correction for proper and consistent CH4 measurements in the TIR: this is the objective of the ESA CH4TIR project. The CH4TIR project brings together the expertise of researchers using different state-of-the-art forward/inverse model and retrieval schemes, namely ASIMUT-ALVL and σ-δ-IASI, to identify the possible causes for the observed biases and quantify the uncertainties linked with the forward modelling and inversion of CH4 in the TIR region. Due to its accurate measurements and well-characterized noise, IASI observations are used as the main data source for the project, but TANSO-FTS observations are also used for comparison. First, we present a sensitivity analysis carried out for the CH4 retrievals performed with IASI and TANSO-FTS data in the TIR region using ASIMUT. We assess the impact of the retrieval spectral range, the measurement uncertainty, uncertainties in the spectroscopic data, and the inclusion of different species in the retrieval. An analysis of the IASI spectral residuals from both ASIMUT-ALVL and σ-δ-IASI retrievals shows that residuals are largest in the strongest part of the Q branch (1300-1310 cm-1), where line mixing effects are most significant. Dedicated laboratory measurements of CH4 lines in this spectral domain are being performed and analysed in the frame of this project. An important feature of the project is to call on two different approaches and tools for the retrieval of CH4 from IASI observations. We therefore characterize the differences between the σ-IASI and ASIMUT-ALVL radiative transfer modelling in the 1190 - 1350 cm-1 region based on 6 AFGL atmospheres. To further assess the error from the forward/inverse model, the results of a round robin exercise is also presented, where the output from one RTM is used as input for the other RTM/inversion scheme. Finally, we explore how critical a priori temperature and H2O profiles are to the accuracy of the CH4 inversion. To investigate this effect, a two-step retrieval approach is used where a first retrieval by σ-δ-IASI exploits the entire IASI spectral range and is used as a priori for the CH4 retrieval performed on a narrower spectral range (1190 – 1350 cm-1). This ongoing work already provides a comprehensive analysis and prioritisation of error sources for the retrieval of CH4 from TIR hyperspectral measurements, emphasizing the critical need of spectroscopy measurements and line interference modelling in the Q branch around 1300 cm-1 for reducing CH4 retrieval biases and improving the retrieval sensitivity in the lowermost levels of the atmosphere.

The 3 IASI instruments on-board the Metop satellites have been sounding the atmospheric composition since 2006. Up to ~30 atmospheric gases can be measured from IASI spectra, allowing monitoring of weather, atmospheric chemistry, and climate. Extreme events such as fires, high pollution episodes, volcanic eruptions, industrial accidents, etc., that impact on the population and the environment have become a major political issue. With IASI providing global observations twice a day in near real time, a new way for the systematic and continuous detection of exceptional atmospheric events to support operational decisions is possible. In this work, we explore and improve an automatic system for the detection and characterization of extreme events, which relies on the principal component analysis (PCA) method. We assess this PCA-based system by analysing IASI raw and compressed spectra along with their differences (residuals) for various past and documented extreme events. The benefits and limitations of this method will be discussed. A new method based on the refined analysis of residuals for the whole year 2019 is proposed, that could be used as an automatic detection method for unexpected events. Finally, we investigate the potential of deep learning methods as a way to compare residuals with a database of extreme event in order to better characterize detected events.

Nitrogen dioxide (NO2) is a good indicator for air quality (AQ) in urban and industrialized areas. The instrument TROPOMI on the ESA S5P satellite provides high quality daily measures of the NO2 tropospheric column from space. The lockdowns that countries and cities have implemented to mitigate the spread of Covid-19 represent an unprecedented reduction of human activities that significantly impacted AQ and NO2 emissions in urban areas. Several communications have associated lockdowns resulting from Covid-19 with a decrease in NO2 air pollution as observed from space by TROPOMI. This relationship between NO2 measurements from space and lockdowns must be further discussed and consolidated: how accurately can we quantify NO2 pollution reduction at city scale during lockdown periods with TROPOMI data only? This work presents a processing of TROPOMI NO2 level 2 data into an original time series – the city-scale NO2 plume mass –- that has been designed to quantify the NO2 pollution level over cities. This is used to verify the impact of lockdowns on NO2 and to examine how urban NO2 pollution reduction associated with lockdown periods may be quantified, on the basis of yearly variations of the TROPOMI city-scale NO2 plume mass and related uncertainty, exploited over representative periods of time. We evaluated this measure and checked the corresponding added value and limitation for quantifying the impact of lockdowns on NO2 variations and separate from other effects, comparing with independent CAMS data and in situ measurements. Building on these results, our second objective was to estimate the reduction on the NO2 tropospheric level over four European cities during their respective lockdowns. The methodology developed may be applied to any major cities and industrial areas. This work should be considered as an initial demonstration for a space-based monitoring system of air quality at the city scale. It may also serve as a testbed for assessing national and international regulations to reduce pollutant levels.

The ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions) product (a climatological database coupled to its companion calculation toolkit) enables users to simulate realistic hyperspectral and directional global Earth surface reflectances (i.e., top-of-canopy/bottom-of-atmosphere) over the 240–4000 nm spectral range (at 1-nm resolution) and in any illumination/observation geometry, at 0.1° × 0.1° spatial resolution for a typical year. ADAM aims to support the preparation of optical Earth observation missions as well as the design of operational processing chains for the retrieval of atmospheric parameters by characterizing the expected surface reflectance, accounting for its anisotropy. Firstly, we describe (1) the methods used in the development of the gridded monthly ADAM climatologies (over land surfaces: monthly means of normalized reflectances derived from MODIS observations in seven spectral bands for the year 2005; over oceans: monthly means over the 1999–2009 period of chlorophyll content from SeaWiFS and of wind speed from SeaWinds), and (2) the underlying modeling approaches of ADAM toolkit to simulate the spectro-directional variations of the reflectance depending on the assigned surface type. Secondly, we evaluate ADAM simulation performances over land surfaces. A comparison against POLDER multi-spectral/multi-directional measurements for year 2008 shows reliable simulation results with root mean square differences below 0.027 and R2 values above 0.9 for most of the 14 land cover IGBP classes investigated, with no significant bias identified. Only for the “Snow and ice” class is the performance lower pointing to a limitation of climatological data to represent actual snow properties. An evaluation of the modeled reflectance in the specific backscatter direction against CALIPSO data reveals that ADAM tends to overestimate (underestimate) the so-called “hot-spot” by a factor of about 1.5 (1.5 to 2) for barren (vegetated) surfaces.

The Paris Agreement of the United Nations Framework Convention on Climate Change is a binding international treaty signed by 196 nations to limit their greenhouse gas emissions through ever-reducing Nationally Determined Contributions and a system of 5-yearly Global Stocktakes in an Enhanced Transparency Framework. To support this process, the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations mainly to monitor anthropogenic carbon dioxide (CO2) emissions. The CO2 Human Emissions (CHE) project has been successfully coordinating efforts of its 22 consortium partners, to advance the development of a European CO2 monitoring and verification support (CO2MVS) capacity for anthropogenic CO2 emissions. Several project achievements are presented and discussed here as examples. The CHE project has developed an enhanced capability to produce global, regional and local CO2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO2 Monitoring Mission (CO2M).

Current nadir-looking thermal infrared (TIR) sounders, such as the Infrared Atmospheric Sounding Interferometer (IASI) launched onboard the MetOp polar-orbiting platforms, are now playing an important role for probing pollutants in the troposphere and in the boundary layer (e.g., carbon monoxide – CO, ozone – O 3 , ammonia, sulfur dioxide). Vertical profiles can be obtained for the main absorbers, with varying vertical resolution and accuracy, depending on geophysical parameters and instrumental specifications.Two future missions using TIR instruments (IRS on Sentinel 4/MTG geostationary-orbiting platform and IASI-NG on Sentinel 5/MetOp-SG polar-orbiting platform) are planned to be launched by EUMETSAT within 5 years. Both instruments are nadir looking Fourier transform spectrometers like IASI but with different radiometric and spectral characteristics.In this study, we illustrate the ability of IASI to monitor CO and O 3 in the lowermost troposphere. We assess more specifically the performances of the different satellite instrument concepts in terms of vertical reso- lution and sensitivity at the surface for CO and O 3 , using representative cases at local, continental and global scales.

Large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely the Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr−1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.