Advances in Earth observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water land-surface models with the capability to simultaneously assimilate several such data streams. The present article discusses the requirements for such a model and presents one such model based on the combination of the existing Data Assimilation Linked Ecosystem Carbon (DALEC) land vegetation carbon cycle model with the Biosphere Energy-Transfer HYdrology (BETHY) land-surface and terrestrial vegetation scheme. The resulting D&B model, made available as a community model, is presented together with a comprehensive evaluation for two selected study sites of widely varying climate. We then demonstrate the concept of land-surface modelling aided by data streams that are available from satellite remote sensing. Here we present D&B with four observation operators that translate model-derived variables into measurements available from such data streams, namely fraction of photosynthetically active radiation (FAPAR), solar-induced chlorophyll fluorescence (SIF), vegetation optical depth (VOD) at microwave frequencies and near-surface soil moisture (also available from microwave measurements). As a first step, we evaluate the combined model system using local observations and finally discuss the potential of the system presented for multi-stream data assimilation in the context of Earth observation systems.

Joint retrieval of vegetation status from synthetic aperture radar (SAR) and optical data holds much promise due to the complimentary of the information in the two wavelength domains. SAR penetrates the canopy and includes information about the water status of the soil and vegetation, whereas optical data contains information about the amount and health of leaves. However, due to inherent complexities of combining these data sources there has been relatively little progress in joint retrieval of information over vegetation canopies. In this study, data from Sentinel-1 and Sentinel-2 were used to invert coupled radiative transfer models to provide synergistic retrievals of leaf area index and soil moisture. Results for leaf area are excellent and enhanced by the use of both data sources (RSME is always less than 0.5 and has a correlation of better than 0.95 when using both together), but results for soil moisture are mixed with joint retrievals generally showing the lowest RMSE but underestimating the variability of the field data. Examples of such synergistic retrieval of plant properties from optical and SAR data using physically based radiative transfer models are uncommon in the literature, but these results highlight the potential for this approach.

Remote sensing observations of variables related to vegetation at microwave and optical/infrared wavelengths are presented over three regions in Europe in the Iberian peninsula, northern Finland and central Europe. They include the instrumented sites of Las Majadas, Sodankyla and Reusel. The final goal is to better constrain land carbon cycle models using the complementarities of vegetation optical depth derived at different frequencies from active and passive instruments (related to vegetation water content and biomass) as well as optical data of the fraction of absorbed photosynthetically active radiation or solar induced fluorescence, closely linked to photosynthesis. The first results confirm this complementarity. For instance, time series of different variables exhibit positive correlations in some areas and negative correlations in other areas.

The Paris Agreement foresees to establish a transparency framework that builds upon inventory-based national greenhouse gas emission reports, complemented by independent emission estimates derived from atmospheric measurements through inverse modelling. The capability of such a Monitoring and Verification Support (MVS) capacity to constrain fossil fuel emissions to a sufficient extent has not yet been assessed. The CO2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO2 concentration (XCO2), is expected to become a key component of an MVS capacity. Here we present a CCFFDAS that operates at the resolution of the CO2M sensor, i.e. 2km by 2km, over a 200 km by 200 km region around Berlin. It combines models of sectorial fossil fuel CO2 emissions and biospheric fluxes with the Community Multiscale Air Quality model (coupled to a model of the plume rise from large power plants) as observation operator for XCO2 and tropospheric column NO2 measurements. Inflow from the domain boundaries is treated as extra unknown to be solved for by the CCFFDAS, which also includes prior information on the process model parameters. We discuss the sensitivities (Jacobian matrix) of simulated XCO2 and NO2 troposheric columns with respect to a) emissions from power plants, b) emissions from the surface and c) the lateral inflow and quantify the respective contributions to the observed signal. The Jacobian representation of the complete modelling chain allows us to evaluate data sets of simulated random and systematic CO2M errors in terms of posterior uncertainties in sectorial fossil fuel emissions. We provide assessments of XCO2 alone and in combination with NO2 on the posterior uncertainty in sectorial fossil fuel emissions for two 1-day study periods, one in winter and one in summer. We quantify the added value of the observations for emissions at a single point, at the 2km by 2km scale, at the scale of Berlin districts, and for Berlin and further cities in our domain. This means the assessments include temporal and spatial scales typically not covered by inventories. Further, we quantify the effect of better information of atmospheric aerosol, provided by a multi-angular polarimeter (MAP) onboard CO2M, on the posterior uncertainties. The assessments differentiate the fossil fuel CO2 emissions into two sectors, an energy generation sector (power plants) and the complement, which we call “other sector”. We find that XCO2 measurements alone provide a powerful constraint on emissions from larger power plants and a constraint on emissions from the other sector that increases when aggregated to larger spatial scales. The MAP improves the impact of the CO2M measurements for all power plants and for the other sector on all spatial scales. Over our study domain, the impact of the MAP is particularly high in winter. NO2 measurements provide a powerful additional constraint on the emissions from power plants and from the other sector.

Abstract. TM2 is a global three-dimensional model of the atmospheric transport of passive tracers. The adjoint of TM2 is a model that allows the efficient evaluation of derivatives of the simulated tracer concentration at obs ervational locations with respect to the tracer’s sources and sinks. We describe the ge neration of the adjoint model by applying the Tangent linear and Adjoint Model Compiler in the reverse mode,of automatic differentiation to the code of TM2. Using CO as an example of a chemically inert tracer, the simulated concentration a t observational locations is linear in the surface exchange fluxes, and thus the transpo rt can be represented by the model’s Jacobian matrix. In many current inverse mode ling studies, such a matrix has been computed,by multiple runs of a transport model for a set of prescribed surface flux patterns. The computational cost ha s been proportional to the number of patterns. In contrast, for differentiation in reverse mode, the cost is independent of the number of flux components. Hence, by a si ngle run of the adjoint model, the Jacobian for the approximately 8 latitude by 10 longitude horizontal resolution of TM2 could be computed,efficiently. We quantify this efficiency by comparison,with the conventional forward mode ling approach. For some prominent observational sites, we present visualizat ions of the Jacobian matrix by series of illustrative global maps quantifying th e impact of potential emissions on the concentration in particular months. Furth ermore, we demonstrate how the Jacobian matrix is employed,to completely analyze a transport model run: A simulated monthly,mean,value at a particular station is decomposed,into the contributions to this value by all flux components, i.e., the fluxes into every surface model grid cell and month. This technique also results in a series of global maps.

The northern land biosphere is believed to be the main global sink of CO2, but the contribution of Europe is uncertain. While bottom‐up estimates and inverse atmospheric transport studies based on atmospheric CO2 observed in situ or from space by OCO‐2 point to a moderate rate of uptake, some other inversions based on remotely sensed atmospheric CO2 from GOSAT/SCIAMACHY and biomass estimates from passive microwave satellite data point to a large sink of around 1 Gt C/yr. We present results from combining both approaches in a data assimilation framework, inverting a biosphere model against in situ atmospheric CO2 and passive microwave measurements. When assimilating all observations, we estimate a European carbon sink of 0.303 ± 0.083 Gt C/yr for 2010–2015. The result agrees with other bottom‐up studies and atmospheric inversions using in situ CO2 or OCO‐2 observations pointing to potential data problems when using observations from GOSAT or SCIAMACHY to estimate the European CO2 sink.

The ongoing ESA funded 'SMOS + Vegetation' project combines a retrieval component that aims at further improving the SMOS VOD product with an assimilation component that aims at demonstrating the added value of this product in constraining simulated land surface fluxes of carbon dioxide. This contribution focuses on the project's modelling and assimilation component. We describe the construction of dedicated observation operators that link the state of the terrestrial biosphere model to simulated VOD and surface layer soil moisture. We present our carbon assimilation system around a terrestrial biosphere model and demonstrate its operation through simultaneous assimilation of the SMOS VOD product over seven sites covering a range of plant functional types.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse ( ∼ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass.

We present the prototype of a a flexible modelling system for Arctic Mission Benefit Analysis (ArcMBA) that evaluates in a mathematically rigorous fashion the observational constraints imposed by individual and groups of EO data products in using the quantitative network design (QND) approach. The assessment of the observation impact (added value) is performed in terms of the uncertainty reduction in a four-week forecast of sea ice and snow volumes (SIV and SNV) for three regions along the Northern Sea Route (NSR) by a coupled model of the sea ice-ocean system, which is extended by observation operators that link the simulated variables to equivalents of sea ice thickness (SIT), sea ice freeboard (SIFB), radar freeboard (RFB), laser freeboard (LFB), and snow depth (SND) products.

. Assimilation of remote-sensing products of sea ice thickness (SIT) into sea ice–ocean models has been shown to improve the quality of sea ice forecasts. Key open questions are whether assimilation of lower-level data products such as radar freeboard (RFB) can further improve model performance and what performance gains can be achieved through joint assimilation of these data products in combination with a snow depth product. The Arctic Mission Benefit Analysis system was developed to address this type of question. Using the quantitative network design (QND) approach, the system can evaluate, in a mathematically rigorous fashion, the observational constraints imposed by individual and groups of data products. We demonstrate the approach by presenting assessments of the observation impact (added value) of different Earth observation (EO) products in terms of the uncertainty

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new L-Band (1.4 GHz) VOD dataset (SMOS-IC L-VOD) obtained from the SMOS (Soil Moisture and Ocean Salinity) satellite. SMOS IC L-VOD is evaluated through a comparison with several vegetation-related quantities such as tree height and above ground biomass (AGB) for different land cover types. SMOS L-VOD shows monotonic relationships with respect to the variables extracted from these different datasets without signs of saturation at high values. The relationships between L-VOD and AGB were also compared to those obtained using the Normalized Difference Vegetation Index (NDVI) and K/X/C-VOD (VOD measured at 19, 10.7, and 6.9 GHz). In contrast to NDVI and K/X/C-VOD, L-VOD shows a relationship to AGB that is closer to a linear one without significant signs of saturation. SMOS L-VOD is a very promising dataset for large scale monitoring of biomass, at coarse scale spatial resolution (~ 40 km), but with high temporal resolution and with an improved sensitivity with respect to higher-frequency VOD data.

We present two novel earth observation products derived from the BESD and EMMA XCO2 products which were respectively retrieved from SCIAMACHY and GOSAT observations within the GreenHouse Gas project of ESA's Climate Change Initiative (GHG-CCI). These products are inferred by a Carbon Cycle Data Assimilation System (CCDAS) and consist of net and gross biosphere-atmosphere fluxes of carbon dioxide on a global 0.5 degrees grid. As a further dataset provided by the CCI, the burnt area product developed by its Firei project was used in the CCDAS to prescribe the emission component from biomass burning. The new flux products are provided with per-pixel uncertainty ranges. Fluxes with uncertainty ranges can also be provided aggregated in space and time, e.g. over given regions or as annual means. For both, posterior flux fields inferred from BESD and EMMA products, transport model simulations show reasonable agreement with the atmospheric carbon dioxide concentration observed at flask sampling stations. This means that the information provided by the terrestrial and transport models, the respective GHG ECV product, the burnt area ECV product, a product of the Fraction of Absorbed Photosynthetically Active Radiation used to drive the model, and the atmospheric flask samples is largely consistent. The most prominent feature in the posterior net flux is the tropical source of CO2 inferred from both products. But for the EMMA product this release, especially over South America, is with 300 gC/m(2)/year much more pronounced than for BESD. This confirms findings by a recent intercomparison of transport inversions using GOSAT data by Houweling et a). (2015). The reason for the larger net flux is increased heterotrophic respiration. For both products the posterior 2010 sink over Europe (without Russia) is in the range of a recent compilation of European flux estimates by Reuter et al. (2016b). The posterior 2010 uptake of Australia (including Oceania) inferred from the EMMA product is 1.3 +/- 0.2 PgC/year and appears to confirm the high sink also derived from GOSAT by Detrners et al. (2015) over a slightly different period and area. While for some regions (USA, Canada, Europe, Russia, Asia) the one standard deviation uncertainty ranges derived from BESD and EMMA do overlap, for some other regions (Brazil, Africa, Australia) this is not the case. It is not clear yet whether this is due to the uncertainty specifications in the respective products or the handling of uncertainty in the assimilation chain. Assumptions on correlation of observational uncertainty in space and time have a considerable impact on the inferred flux fields (a 60 gC/m(2)/year). The effect of adding an uncertainty that approximates the error in the retrieval system is of similar size.

Assessing the inherent uncertainties in satellite data products is a challenging task. Different technical approaches have been developed in the Earth Observation (EO) communities to address the validation problem which results in a large variety of methods as well as terminology. This paper reviews state-of-the-art methods of satellite validation and documents their similarities and differences. First, the overall validation objectives and terminologies are specified, followed by a generic mathematical formulation of the validation problem. Metrics currently used as well as more advanced EO validation approaches are introduced thereafter. An outlook on the applicability and requirements of current EO validation approaches and targets is given.

We present the development of the Adjoint of the Global Eulerian–Lagrangian Coupled Atmospheric (A-GELCA) model that consists of the National Institute for Environmental Studies (NIES) model as an Eulerian three-dimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian Particle Dispersion Model (LPDM). The forward tangent linear and adjoint components of the Eulerian model were constructed directly from the original NIES TM code using an automatic differentiation tool known as TAF (Transformation of Algorithms in Fortran; http://www.FastOpt.com), with additional manual pre- and post-processing aimed at improving transparency and clarity of the code and optimizing the performance of the computing, including MPI (Message Passing Interface). The Lagrangian component did not require any code modification, as LPDMs are self-adjoint and track a significant number of particles backward in time in order to calculate the sensitivity of the observations to the neighboring emission areas. The constructed Eulerian adjoint was coupled with the Lagrangian component at a time boundary in the global domain. The simulations presented in this work were performed using the A-GELCA model in forward and adjoint modes. The forward simulation shows that the coupled model improves reproduction of the seasonal cycle and short-term variability of CO2. Mean bias and standard deviation for five of the six Siberian sites considered decrease roughly by 1 ppm when using the coupled model. The adjoint of the Eulerian model was shown, through several numerical tests, to be very accurate (within machine epsilon with mismatch around to ±6 e−14) compared to direct forward sensitivity calculations. The developed adjoint of the coupled model combines the flux conservation and stability of an Eulerian discrete adjoint formulation with the flexibility, accuracy, and high resolution of a Lagrangian backward trajectory formulation. A-GELCA will be incorporated into a variational inversion system designed to optimize surface fluxes of greenhouse gases.

We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the JSBACH land-surface scheme, which is part of the MPI-Earth System Model v1. The simulated phenology and net land carbon balance were constrained by globally distributed observations of the fraction of absorbed photosynthetically active radiation (FAPAR, using the TIP-FAPAR product) and atmospheric CO2 at a global set of monitoring stations for the years 2005 to 2009. When constrained by FAPAR observations alone, the system successfully, and computationally efficiently, improved simulated growing-season average FAPAR, as well as its seasonality in the northern extra-tropics. When constrained by atmospheric CO2 observations alone, global net and gross carbon fluxes were improved, despite a tendency of the system to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, thereby increasing the overall appropriateness of the simulated biosphere dynamics and underlying parameter values. Our study thus demonstrates the value of multiple-data-stream assimilation for the simulation of terrestrial biosphere dynamics. It further highlights the potential role of remote sensing data, here the TIP-FAPAR product, in stabilising the strongly underdetermined atmospheric inversion problem posed by atmospheric transport and CO2 observations alone. Notwithstanding these advances, the constraint of the observations on regional gross and net CO2 flux patterns on the MPI-CCDAS is limited through the coarse-scale parametrisation of the biosphere model. We expect improvement through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO2) and methane (CH4) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Data Package No. 3 (CRDP3). We focus on the GHG-CCI project core data products, which are near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 (in ppm) and XCH4 (in ppb) retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode radiance observations in the near-infrared/shortwave-infrared spectral region. The GHG-CCI products are primarily individual sensor Level 2 products. However, we also generate merged Level 2 products ("EMMA products"). Here we also present a first GHG-CCI Level 3 product, namely XCO2 and XCH4 in Obs4MIPs format (monthly, 5°×5°).

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas contributing to about half of the total anthropogenic change in the Earth's radiation budget. And about half of the anthropogenic CO2 emissions stay in the atmosphere, the remainder is taken up by the biosphere. It is of paramount importance to better understand CO2 sources and sinks and their spatio-temporal distribution. In the context of climate change this information is needed to improve the projections of future trends in carbon sinks and sources. Since the terrestrial carbon and water cycles are tightly coupled by biological plant processes, i.e. through the stomatal gas exchange with the atmosphere, it is expected that information on the soil moisture state will help to constrain terrestrial carbon fluxes. In the present feasibility study we employ the Carbon Cycle Data Assimilation System CCDAS to pioneer the assimilation of the SMOS L3 soil moisture product together with another biophysical data set — in this case atmospheric CO2 flask samples. The two data streams are assimilated into a process-based model of the terrestrial carbon cycle over two years. CCDAS aims to optimise model process parameters and subsequently land surface CO2 exchange fluxes. We find that the assimilation of SMOS data improves the agreement with independent soil moisture data from the active ASCAT instrument. In both cases the assimilation also improves the fit of modelled atmospheric CO2 to the observations at flask sampling sites which have not been used in the assimilation. Reduction of uncertainty relative to the prior is generally high for both regional net ecosystem productivity and net primary productivity and considerably higher than for assimilating CO2 only, which quantifies the added value of SMOS observations as a constraint on the terrestrial carbon cycle. The study demonstrates a high potential for a SMOS L4 carbon flux product.