Hao Zuo

The new eddy-permitting ORAP5 ocean reanalysis: description, evaluation and uncertainties in climate signals

A new eddy-permitting ocean reanalysis has been recently completed at ECMWF. It is called Ocean ReAnalysis Pilot 5 (ORAP5), and it spans the period 1979–2012. This work describes the new system, evaluates its performance, and investigates how the estimation of climate indices are affected by the assimilation system settings. ORAP5 introduces several upgrades with respect to its predecessor ORAS4, including increased horizontal and vertical resolution, an prognostic sea-ice component, new versions of the ocean and data assimilation system, revised surface fluxes, new version and treatment of satellite sea surface height data, and assimilation of sea-ice concentration, among others. ORAP5 shows similar performance to ORAS4, with improvements in the northern extratropics (especially in salinity), and slight degradation in the Southern Ocean, probably because the observations are insufficient to constrain the increased level of variability in ORAP5. The sensitivity experiments show that superobbing of altimeter data and correlation length-scales of the background errors have a visible impact on the time evolution of global steric height and its partition into thermo/halo-steric contributions. The sensitivities are especially large in the pre-Argo period, when there is the risk of producing unrealistic steric height variations by overfitting the altimeter data. Compared with a control run without data assimilation, all the assimilation experiments also show stronger variability in the halosteric component in the pre-Argo period. The results highlight the importance of sub-surface observations to assist the assimilation of altimeter data, and the need of using a variety of metrics for evaluating ocean reanalysis systems.

Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project

AbstractOcean–sea ice reanalyses are crucial for assessing the variability and recent trends in the Arctic sea ice cover. This is especially true for sea ice volume, as long-term and large scale sea ice thickness observations are inexistent. Results from the Ocean ReAnalyses Intercomparison Project (ORA-IP) are presented, with a focus on Arctic sea ice fields reconstructed by state-of-the-art global ocean reanalyses. Differences between the various reanalyses are explored in terms of the effects of data assimilation, model physics and atmospheric forcing on properties of the sea ice cover, including concentration, thickness, velocity and snow. Amongst the 14 reanalyses studied here, 9 assimilate sea ice concentration, and none assimilate sea ice thickness data. The comparison reveals an overall agreement in the reconstructed concentration fields, mainly because of the constraints in surface temperature imposed by direct assimilation of ocean observations, prescribed or assimilated atmospheric forcing and assimilation of sea ice concentration. However, some spread still exists amongst the reanalyses, due to a variety of factors. In particular, a large spread in sea ice thickness is found within the ensemble of reanalyses, partially caused by the biases inherited from their sea ice model components. Biases are also affected by the assimilation of sea ice concentration and the treatment of sea ice thickness in the data assimilation process. An important outcome of this study is that the spatial distribution of ice volume varies widely between products, with no reanalysis standing out as clearly superior as compared to altimetry estimates. The ice thickness from systems without assimilation of sea ice concentration is not worse than that from systems constrained with sea ice observations. An evaluation of the sea ice velocity fields reveals that ice drifts too fast in most systems. As an ensemble, the ORA-IP reanalyses capture trends in Arctic sea ice area and extent relatively well. However, the ensemble can not be used to get a robust estimate of recent trends in the Arctic sea ice volume. Biases in the reanalyses certainly impact the simulated air–sea fluxes in the polar regions, and questions the suitability of current sea ice reanalyses to initialize seasonal forecasts.

An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project

A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993–2011. The main differences among the reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean parameter to another, especially in global indices. However, despite several caveats in the design of the multi-system ensemble, the main conclusion from this study is that an eddy-permitting multi-system ensemble approach has become mature and our results provide a first step towards a systematic comparison of eddy-permitting global ocean reanalyses aimed at providing robust conclusions on the recent evolution of the oceanic state.

SEAS5: The new ECMWF seasonal forecast system

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, 5 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 ENSO amplitude and an improvement in ENSO prediction skill over the central-west Pacific. Improvements in two-metre temperature skill are also clear over the tropical Pacific. SST biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution 10 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 two-metre 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 two-metre temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, 15 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 summer. In summary, development and added complexity since System 4 has ensured SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in ENSO prediction. Copyright statement.