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In this presentation we introduce coupled assimilation activities conducted in support of seamless Earth system approach developments for Numerical Weather Prediction and climate reanalysis at the European Centre for Medium-Range Weather Forecasts (ECMWF). For operational applications coupled assimilation requires to have reliable and timely access to observations in all the Earth system components and it relies on consistent acquisition and monitoring approaches across the components. We show recent and future infrastructure developments and implementations to support consistent observations acquisition and monitoring for land and ocean at ECMWF. We discuss challenges of surface sensitive observations assimilation and we show ongoing forward operator and coupling developments to enhance the exploitation of interface observations over land and ocean surfaces. We present plans to use new and future observation types from future observing systems such as the Copernicus Expansion missions.

The Copernicus Climate Change Service (C3S), operated by ECMWF on behalf of the European Commission, provides climate services built around a comprehensive suite of data products. These products include multidecadal estimates of the atmospheric state, based on atmospheric reanalysis, and a range of observational datasets on Essential Climate Variables (ECVs). Atmospheric reanalyses are now regarded as valuable sources of information for monitoring trends in the global atmospheric state and employ highly optimised methods for combining observations of meteorological variables, both in-situ and satellite. The most recent C3S global atmospheric reanalysis, ERA5, covering the period 1979-2019 (to be extended to 1950) is now available and since its release in early 2019 has a rapidly growing user base, currently numbering more than 30,000. It uses a recent version of the ECMWF Numerical Weather Prediction (NWP) system to assimilate observations (87 billion for the period 1979 - 2018) in order to analyse the atmospheric state. Satellite observations are a key input to reanalyses and the range of observations assimilated are reviewed. ECVs derived from satellite and in-situ observations, spanning land, atmosphere, ocean and biosphere domains, produced as part of international collaborations, are available via the C3S Climate Data Store (CDS). The aspiration of C3S is to further develop the CDS to include a wider range of (∼ 35) ECVs in the next phase of the Copernicus programme (2021-2027).

Reanalysis is a key contribution to the Copernicus Climate Change Service (C3S) that is implemented at the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. The most recent ECMWF reanalysis, ERA5, provides hourly estimates of the global atmosphere, land surface and ocean waves at a horizontal resolution of 31km. Daily updates are provided with a latency of 5 days, while an extension back to 1950 is to be made available in the 2nd quarter of 2020.ERA5 uses a 2016 version of the ECMWF numerical weather prediction model and data assimilation system (Integrated Forecasting System Cy41r2) to assimilate both in situ and satellite observations (95 billion for the period 1979 - 2019), many of which stem from reprocessed data records. The assimilation method includes a variational method for estimating observation biases that respects the heterogeneity within the observing system. Information on random uncertainties in the state estimates is provided by a 10-member ensemble of data assimilations at half the horizontal resolution (63km).This presentation provides a concise overview of the ERA5 data assimilation system. A basic evaluation of characteristics and performance is presented, which includes an inter-comparison with other reanalysis products, such as its predecessor ERA-Interim and several major reanalyses produced elsewhere. Attention is given to the importance of the specification of the background error covariance matrix that determines the weight given to the model's first guess in the assimilation. In addition, a special focus will be on the back extension from 1950 to 1978, where the absence of satellite data prior to the 1970s puts a more demanding constraint on the data assimilation system.

International audience; Abstract. Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020).

Carbon dioxide (CO2) and methane (CH4) are important atmospheric greenhouse gases (GHG) and, therefore, classified as essential climate variables (ECVs). Previously, satellite-derived atmospheric CO2 and methane CH4 ECV data sets have been generated and made available via the GHG-CCI project of the European Space Agency’s (ESA) Climate Change Initiative (CCI, http://www.esa-ghg-cci.org/ ). The latest GHG-CCI data set, Climate Research Data Package No. 4 (CRDP 4), covers the time period 2003–2015 and is available since February 2017. Currently, the production and provision of these data sets is being continued (pre-)operationally via the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/ ), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. The C3S satellite GHG sub-project (C3S_312a_Lot6) is led by University of Bremen supported by University of Leicester (UK), SRON (The Netherlands) and CNRS-LMD (France). The first Climate Data Record (CDR) data set produced and delivered within the C3S framework covers the time period 2003–2016 and consists of column-average dry-air mole fraction CO2 and CH4 products, i.e., XCO2 and XCH4, from SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. Furthermore, mid-tropospheric CO2 and CH4 mixing ratios from IASI Metop-A and Metop-B are part of this data set. It is planned to extend this data set each year by one additional year. The data products are available via the Climate Data Store (CDS) of C3S. Here a short overview about this new Earth Observation data set is presented.

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.

The Sentinel 5 precursor satellite was successfully launched on 13th October 2017, carrying the Tropospheric Monitoring Instrument TROPOMI as its single payload. TROPOMI is the next generation atmospheric sounding instrument, continuing the successes of GOME, SCIAMACHY, OMI and OMPs, with higher spatial resolution, improved sensitivity and extended wavelength range. The instrument contains four spectrometers, divided over two modules sharing a common telescope, measuring the ultraviolet, visible, near-infrared and shortwave infrared reflectance of the Earth. The imaging system enables daily global coverage using a push-broom configuration, with a spatial resolution as low as 7 × 3.5 km2 in nadir from a Sun-synchronous orbit at 824 km and an equator crossing time of 13:30 local solar time. This article reports the pre-launch calibration status of the TROPOMI payload as derived from the on-ground calibration effort. Stringent requirements are imposed on the quality of on-ground calibration in order to match the high sensitivity of the instrument. In case that the systematic errors that originate from the calibration exceed the random errors in the observations, the scientific products may be compromised. A new methodology has been employed during the analysis of the obtained calibration measurements to ensure the consistency and validity of the calibration. This was achieved by using the production grade Level 0 to 1b data processor in a closed-loop validation setup. Using this approach the consistency between the calibration and the L1b product could be established, as well as confidence in the obtained calibration result. This article introduces this novel calibration approach, and describes all relevant calibrated instrument properties as they were derived before launch of the mission. For most of the relevant properties compliancy with the requirements could be established, including the knowledge of the instrument spectral and spatial response functions, and the absolute radiometric calibration. Partial compliancy was established for the straylight correction; especially the out-of-spectral-band correction for the NIR channel needs further validation. Incompliance was reported for the relative radiometric calibration of the Sun port diffusers. These latter two subjects will be addressed during the in-flight commissioning phase in the first 6 months following launch.

The abstract is available here: https://uscholar.univie.ac.at/o:1049617

The European Reanalysis of Global Climate Observations 2 (ERA-CLIM2) is a European Union Seventh Framework Project started in January 2014 and due to be completed in December 2017. It aims to produce coupled reanalyses, which are physically consistent datasets describing the evolution of the global atmosphere, ocean, land surface, cryosphere, and the carbon cycle. ERA-CLIM2 has contributed to advancing the capacity for producing state-of-the-art climate reanalyses that extend back to the early twentieth century. ERA-CLIM2 has led to the generation of the first European ensemble of coupled ocean, sea ice, land, and atmosphere reanalyses of the twentieth century. The project has funded work to rescue and prepare observations and to advance the data-assimilation systems required to generate operational reanalyses, such as the ones planned by the European Union Copernicus Climate Change Service. This paper summarizes the main goals of the project, discusses some of its main areas of activities, and presents some of its key results.