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The drive to develop environmental prediction systems that are seamless across both weather and climate timescales has culminated in the development and use of Earth system models, which include a coupled representation of the atmosphere, land, ocean and sea ice, for medium-range weather forecasts. One region where such a coupled Earth system approach has the potential to significantly influence the skill of weather forecasts is in the polar and sub-polar seas, where fluxes of heat, moisture and momentum are strongly influenced by the position of the sea ice edge. In this study we demonstrate that using a dynamically coupled ocean and sea ice model in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System results in improved sea ice edge position forecasts in the Northern Hemisphere in the medium range. Further, this improves forecasts of boundary layer temperature and humidity downstream of the sea ice edge in some regions during periods of rapid change in the sea ice, compared to forecasts in which the sea surface temperature anomalies and sea ice concentration do not evolve throughout the forecasts. However, challenges remain, such as large errors in the position of the ice edge in the ocean analysis used to initialise the ocean component of the coupled system, which has an error of approximately 50 % of the total forecast error at day 9, suggesting there is much skill to be gained by improving the ocean analysis at and around the sea ice edge. The importance of the choice of sea ice analysis for verification is also highlighted, with a call for more guidance on the suitability of satellite sea ice products to verify forecasts on daily to weekly timescales and on meso-scales (< 500 km).

Tropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.

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 ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This manuscript gives a full description of the OCEAN5 system, with the focus on upgrades of system components with respect to its predecessors ORAS4 and ORAP5. An important novelty in OCEAN5 is the ensemble generation strategy that includes perturbation of initial conditions, and a generic perturbation scheme for observations and forcing fields. Other upgrades include revisions to the a-priori bias correction scheme, observation quality control and assimilation method for sea-level anomaly. The OCEAN5 historical reconstruction of the ocean and sea-ice state is the ORAS5 reanalysis, which includes 5 ensemble members and covers the period from 1979 onwards, and with a backward extension until 1958. Updated version of observation data sets are used in ORAS5 production, with special attention devoted to the consistency of sea surface temperature (SST) and sea-ice observations. Assessment of ORAS5 in the observation space suggests that assimilation of observations contribute to reducing the analysis error, with the most prominent contribution from direct assimilation of ocean in-situ observations. Results of observing system experiment further suggest that Argo float is the most influential observation type in our data assimilation system. Assessment of ORAS5 has also been carried out with several key ocean state variables and verified against independent observation data sets from ESA CCI project. With respect to ORAS4, ORAS5 has improved ocean climate state and variability in terms of SST and sea-level, mostly due to increased model resolution and updates in assimilated observation data sets. In spite of the improvements, ORAS5 still underestimates the temporal variance of sea level, and continue exhibiting large SST biases in the Gulf Stream and extension regions which is possibly associated with misrepresentation of front positions. Overall, the SST and sea-ice uncertainties estimated using five ORAS5 ensemble members have spatial patterns consistent with those of analysis error. The ensemble spread of sea-ice is commensurable with the sea-ice analysis error. On the contrary, the ensemble is under-dispersive for SST.

The ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This paper gives a full description of the OCEAN5 system, with the focus on upgrades of system components with respect to its predecessors, ORAS4 and ORAP5. An important novelty in OCEAN5 is the ensemble generation strategy that includes perturbation of initial conditions and a generic perturbation scheme for observations and forcing fields. Other upgrades include revisions to the a priori bias correction scheme, observation quality control and assimilation method for sea-level anomalies. The OCEAN5 historical reconstruction of the ocean and sea-ice state is the ORAS5 reanalysis, which includes five ensemble members and covers the period from 1979 onwards. Updated versions of observation data sets are used in ORAS5 production, with special attention devoted to the consistency of sea surface temperature (SST) and sea-ice observations. Assessment of ORAS5 through sensitivity experiments suggests that all system components contribute to an improved fit to observation in reanalyses, with the most prominent contribution from direct assimilation of ocean in situ observations. Results of observing system experiments further suggest that the Argo float is the most influential observation type in our data assimilation system. Assessment of ORAS5 has also been carried out for several key ocean state variables and verified against reference climate data sets from the ESA CCI (European Space Agency Climate Change Initiative) project. With respect to ORAS4, ORAS5 has improved ocean climate state and variability in terms of SST and sea level, mostly due to increased model resolution and updates in assimilated observation data sets. In spite of the improvements, ORAS5 still underestimates the temporal variance of sea level and continues exhibiting large SST biases in the Gulf Stream and its extension regions which are possibly associated with misrepresentation of front positions. Overall, the SST and sea-ice uncertainties estimated using five ORAS5 ensemble members have spatial patterns consistent with those of analysis error. The ensemble spread of sea ice is commensurable with the sea-ice analysis error. On the contrary, the ensemble spread is under-dispersive for SST.

In this paper we describe SEAS5, ECMWF's fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea-ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the equatorial Pacific cold tongue bias, which is accompanied by a more realistic El Niño amplitude and an improvement in El Niño prediction skill over the central-west Pacific. Improvements in 2 m temperature skill are also clear over the tropical Pacific. Sea-surface temperature (SST) biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF 2 m temperature prediction skill in this region. The prognostic sea-ice model improves seasonal predictions of sea-ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in 2 m temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in JJA. In summary, development and added complexity since System 4 has ensured that SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in the El Niño Southern Oscillation (ENSO) prediction.

This paper explores the sensitivity of hindcasts of the Madden Julian Oscillation (MJO) to the use of different sea surface temperture (SST) products as lower boundary conditions in the European Centre for Medium-range Weather Forecasts (ECMWF) atmospheric model. Three sets of monthly hindcast experiments are conducted, starting from initial conditions from the ERA interim reanalysis. First, as a reference, the atmosphere is forced by the SST used to produce ERA interim. In the second and third experiments, the SST is switched to the OSTIA (Operational Sea Surface Temperature and Sea-Ice Analysis) and the AVHRR-only (Advanced Very High Resolution Radiometer) reanalyses, respectively. Tests on the temporal resolution of the SST show that monthly fields are not optimal, while weekly and daily resolutions provide similar MJO scores. When using either OSTIA or AVHRR, the propagation of the MJO is degraded and the resulting scores are lower than in the reference experiment. Further experiments show that this loss of skill cannot be attributed to either the difference in mean state or temporal variability between the SST products. Additional diagnostics show that the phase relationship between either OSTIA or AVHRR SST and the MJO convection is distorted with respect to satellite observations and the ERA interim reanalysis. This distortion is expected to impact the MJO hindcasts, leading to a relative loss of forecast skill. A realistic representation of ocean–atmosphere interactions is thus needed for MJO hindcasts, but not all SST products – though accurate for other purposes – fulfill this requirement.

In order to identify potential de novo enzyme templates for the cleavage of C–C single bonds in long-chain hydrocarbons, we analyzed protein structures that bind substrates containing alkyl and alkenyl functional groups. A survey of ligand-containing protein structures deposited in the Protein Data Bank resulted in 874 entries, consisting of 194 unique ligands that have ≥10 carbons in a linear chain. Fatty acids and phospholipids are the most abundant types of ligands. Hydrophobic amino acids forming α-helical structures frequently line the binding pockets. Occupation of these binding sites was evaluated by calculating both the buried surface area and volume employed by the ligands; these quantities are similar to those computed for drug–protein complexes. Surface complementarity is relatively low due to the nonspecific nature of the interaction between the long-chain hydrocarbons and the hydrophobic amino acids. The selected PDB structures were annotated on the basis of their SCOP and EC identification numbers, which will facilitate design template searches based on structural and functional homologies. Relatively low surface complementarity and ∼55% volume occupancy, also observed in synthetic-host, alkane-guest systems, suggest general principles for the recognition of long-chain linear hydrocarbons.

Quantifying the effect of the seawater density changes on sea level variability is of crucial importance for climate change studies, as the sea level cumulative rise can be regarded as both an important climate change indicator and a possible danger for human activities in coastal areas. In this work, as part of the Ocean Reanalysis Intercomparison Project, the global and regional steric sea level changes are estimated and compared from an ensemble of 16 ocean reanalyses and 4 objective analyses. These estimates are initially compared with a satellite-derived (altimetry minus gravimetry) dataset for a short period (2003-2010). The ensemble mean exhibits a significant high correlation at both global and regional scale, and the ensemble of ocean reanalyses outperforms that of objective analyses, in particular in the Southern Ocean. The reanalysis ensemble mean thus represents a valuable tool for further analyses, although large uncertainties remain for the inter-annual trends. Within the extended intercomparison period that spans the altimetry era (1993-2010), we find that the ensemble of reanalyses and objective analyses are in good agreement, and both detect a trend of the global steric sea level of 1.0 and 1.1 +/- 0.05 mm/year, respectively. However, the spread among the products of the halosteric component trend exceeds the mean trend itself, questioning the reliability of its estimate. This is related to the scarcity of salinity observations before the Argo era. Furthermore, the impact of deep ocean layers is non-negligible on the steric sea level variability (22 and 12 % for the layers below 700 and 1500 m of depth, respectively), although the small deep ocean trends are not significant with respect to the products spread.

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon’s rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3–6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean.

A new operational ocean reanalysis system (ORAS4) has been implemented at ECMWF. It spans the period 1958 to the present. This article describes its main components and evaluates its quality. The adequacy of ORAS4 for the initialization of seasonal forecasts is discussed, along with the robustness of some prominent climate signals. ORAS4 has been evaluated using different metrics, including comparison with observed ocean currents, RAPID-derived transports, sea-level gauges, and GRACE-derived bottom pressure. Compared to a control ocean model simulation, ORAS4 improves the fit to observations, the interannual variability, and seasonal forecast skill. Some problems have been identified, such as the underestimation of meridional overturning at 26°N, the magnitude of which is shown to be sensitive to the treatment of the coastal observations. ORAS4 shows a clear and robust shallowing trend of the Pacific Equatorial thermocline. It also shows a clear and robust nonlinear trend in the 0–700 m ocean heat content, consistent with other observational estimates. Some aspects of these climate signals are sensitive to the choice of sea-surface temperature product and the specification of the observation-error variances. The global sea-level trend is consistent with the altimeter estimate, but the partition into volume and mass variations is more debatable, as inferred by discrepancies in the trend between ORAS4- and GRACE-derived bottom pressure.

International audience; Ocean data assimilation has reached a sufficient level of maturity such that observations are routinely combined with model forecasts to produce a variety of ocean products. Approaches to ocean data assimilation vary widely both in terms of the sophistication of the method and the observations assimilated, and also in terms of specification of the forecast error covariances, model biases, observation errors, and quality-control procedures. In this paper, we describe some of the ocean data assimilation systems that have been developed within the Global Ocean Data Assimilation Experiment (GODAE) community. We discuss assimilation methods, observations assimilated, and techniques used to specify error covariances. In addition, we describe practical implementation aspects and present analysis performance results for some of the analysis systems. Finally, we describe plans for improving the assimilation systems in the post-GODAE time period beyond 2008.

Uncertainty in ocean analysis methods and deficiencies in the observing system are major obstacles for the reliable reconstruction of the past ocean climate. The variety of existing ocean reanalyses is exploited in a multi-reanalysis ensemble to improve the ocean state estimation and to gauge uncertainty levels. The ensemble-based analysis of signal-to-noise ratio allows the identification of ocean characteristics for which the estimation is robust (such as tropical mixed-layer-depth, upper ocean heat content), and where large uncertainty exists (deep ocean, Southern Ocean, sea ice thickness, salinity), providing guidance for future enhancement of the observing and data assimilation systems.

The NEMO general circulation ocean model is extended to incorporate three physical processes related to ocean surface waves, namely the surface stress (modified by growth and dissipation of the oceanic wave field), the turbulent kinetic energy flux from breaking waves, and the Stokes-Coriolis force. Experiments are done with NEMO in ocean-only (forced) mode and coupled to the ECMWF atmospheric and wave models. Ocean-only integrations are forced with fields from the ERA-Interim reanalysis. All three effects are noticeable in the extra-tropics, but the sea-state dependent turbulent kinetic energy flux yields by far the largest difference. This is partly because the control run has too vigorous deep mixing due to an empirical mixing term in NEMO. We investigate the relation between this ad hoc mixing and Langmuir turbulence and find that it is much more effective than the Langmuir parameterization used in NEMO. The biases in sea surface temperature as well as subsurface temperature are reduced, and the total ocean heat content exhibits a trend closer to that observed in a recent ocean reanalysis (ORAS4) when wave effects are included. Seasonal integrations of the coupled atmosphere-wave-ocean model consisting of NEMO, the wave model ECWAM and the atmospheric model of ECMWF similarly show that the sea surface temperature biases are greatly reduced when the mixing is controlled by the sea state and properly weighted by the thickness of the uppermost level of the ocean model. These wave-related physical processes were recently implemented in the operational coupled ensemble forecast system of ECMWF., Comment: 29 pp, 10 figures, 2 tables in J Geophys Res, 2015

The MKS-EOS proposed by Chu et al. (1992) has been further improved in order to apply it to near-critical, polar, and highly asymmetric gas condensate mixtures. The Kurihara density-independent local-composition mixing rule for the energy parameter a has been included (Kurihara et al., 1987), and test results for selected high quality VLE data indicate that a net improvement is obtained for polar mixtures.

In this study we present results of decadal climate hindcasts over the period 1960-2000 carried out with the ECMWF coupled system IFS/Nemo in which both atmosphere and ocean are initialised to bring the state of the coupled model close to the observed state. The ocean conditions have been produced with NEMOVAR, a multivariate 3D-Var data assimilation method. The atmosphere and land surface initialization was from the ERA-40 and ERA-Interim reanalysis. The skill of the model in reproducing the observed coupled teleconnection patterns and the leading modes of interannual variability in the atmosphere is evaluated. An assessment of the extent to which near-surface air temperature is are skilfully predicted in the forecast range from one to ten years is shown as well.