Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis which, once completed, will embody a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards. This new reanalysis replaces the ERA‐Interim reanalysis (spanning 1979 onwards) which was started in 2006. ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA‐Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3‐hourly at half the horizontal resolution). This paper describes the general set‐up of ERA5, as well as a basic evaluation of characteristics and performance, with a focus on the dataset from 1979 onwards which is currently publicly available. Re‐forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA‐Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global‐mean correlation with monthly‐mean GPCP data is increased from 67% to 77%. In general, low‐frequency variability is found to be well represented and from 10 hPa downwards general patterns of anomalies in temperature match those from the ERA‐Interim, MERRA‐2 and JRA‐55 reanalyses.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

The monthly exchange of ocean wave model data has successfully been taking place among five operational weather centers. The data are compared with observations obtained from moored buoys and platforms. The analysis of 3 yr of data has helped to quantify the global and regional skills, strengths, and weaknesses of the different ocean wave forecasting systems. Since the quality of ocean wave forecasts is intrinsically linked to the quality of the forcing wind fields, it is not surprising to find that the center with the lowest wind speed errors also has the lowest wave height errors. The benefit of using a third-generation Wave Model (WAM), for example, is not so tangible in terms of wave height statistics but it is definitively evident in terms of peak periods. Even though WAM has proved to be well suited for global wave forecasting, it is also clear that research is still needed to reduce the model tendency to underpredict some storms when it is forced by operational global wind fields. It appea... Abstract The monthly exchange of ocean wave model data has successfully been taking place among five operational weather centers. The data are compared with observations obtained from moored buoys and platforms. The analysis of 3 yr of data has helped to quantify the global and regional skills, strengths, and weaknesses of the different ocean wave forecasting systems. Since the quality of ocean wave forecasts is intrinsically linked to the quality of the forcing wind fields, it is not surprising to find that the center with the lowest wind speed errors also has the lowest wave height errors. The benefit of using a third-generation Wave Model (WAM), for example, is not so tangible in terms of wave height statistics but it is definitively evident in terms of peak periods. Even though WAM has proved to be well suited for global wave forecasting, it is also clear that research is still needed to reduce the model tendency to underpredict some storms when it is forced by operational global wind fields. It appea...

Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state climate data record, the Global Climate Observing System has listed “Sea State” as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data, last access: 25 August 2020) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/ (last access: 25 August 2020). Three products are available: a multi-mission along-track L2P product (http://dx.doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (http://dx.doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).

We present an investigation of the performance of the ECMWF coupled atmosphere‐waves‐ocean model for different ocean and atmosphere resolutions on a series of tropical cyclones in the Western Pacific with the aim to better understand the coupled feedback mechanisms in these extreme conditions. For some of the test cases, we only find little impact of coupling the atmosphere to the ocean, while in others, we observe a very large impact. To further understand these differences, we have selected two tropical cyclones (TCs) as case studies: TC Haiyan (with small impact of coupling) and TC Neoguri (with large impact of coupling). The comparison between these two cases suggests that the upper ocean stratification is the key in determining the strength of the coupled feedback. A strong coupled feedback is found whenever the ocean heat content of the upper layer is low while a very weak coupled feedback is found whenever the ocean has a thick warm mixed layer. The oceanographic response to tropical cyclones for the two storms has been compared to sea surface temperature and derived surface currents from drifting buoys and to subsurface observations from Argo and ship launched XBT's. These comparisons show that we are able to realistically reproduce the atmospheric and oceanographic interaction during tropical cyclone conditions which gives us confidence that the coupled modeling system is physically sound.

A deep-water approximation of the Stokes drift velocity profile is explored as an alternative to the monochromatic profile. The alternative profile investigated relies on the same two quantities required for the monochromatic profile, namely, the Stokes transport and the surface Stokes drift velocity. Comparisons with parametric spectra and profiles under wave spectra from the Interim ECMWF Re-Analysis (ERA-Interim) and buoy observations reveal much better agreement than the monochromatic profile even for complex sea states. That the profile gives a closer match and a more correct shear has implications for ocean circulation models since the Coriolis–Stokes force depends on the magnitude and direction of the Stokes drift profile, and Langmuir turbulence parameterizations depend sensitively on the shear of the profile. The alternative profile comes at no added numerical cost compared to the monochromatic profile.