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The BS-MFC (Black Sea Monitoring and Forecasting Centre) delivers near real time and multi-year products for the Black Sea region with the scope to describe its physical, biogeochemical and wave conditions in the frame of CMEMS. This is done through 3 Production Units – Physics, Biogeochemistry and Waves – that implement state-of-the-art and accurate modelling approaches for forecasting and monitoring purposes. In 2019, the BS-MFC offer has been updated to include i) updated versions of the BS-PHY and BS-WAV NRT products and new BS-BIO product, ii) update of the MY products timeseries up to Dec 2018 and iii) inclusion of Ocean Monitoring Indicators (OMI).Considering NRT systems, the systems are performing assimilation of in-situ and satellite products provided by CMEMS TACs with PHY and BIO products centered to 12:00Z and WAV product instantaneous fields covering 10-days forecast. BS-BIO offers new product since Jul 2019, including CHL, PHYC, O2, NO3, PO4, Primary Production and carbonate system components (pH, DIC, Alkalinity, air-sea flux of CO2). To support ocean monitoring purposes, describing the current state of the Black Sea physical dynamics, environmental and extreme events, the BS-MFC implements a set of OMI: a) vertically integrated oxygen content, b) oxygen penetration density and depth, c) sea surface temperature and salinity anomalies, d) significant wave height extremes.To improve forecasting capabilities and prepare the next generation of BS products, the BS-MFC is working on several scientific topics, the most challenged are the increased resolution in vertical of the physical system, the problem of the Bosporus Strait as boundary condition, improved data assimilation capabilities, coupling strategies among PHY, BIO and WAV and improvement of upstream data ingestion in NRT and MY systems, including the usage of hourly forcing in WAV production system and forecast data of the Danube River discharge and nutrients in the PHY and BIO systems. Furthermore, the BS-MFC is working on enforce operational capacities and define pre-operational evaluation to estimate accuracy of operational and new products.

The observational network around the North Atlantic has improved significantly over the last few decades with subsurface profiling floats and satellite observations, and the recent efforts to monitor the Atlantic Meridional Overturning Circulation (AMOC). These have shown decadal timescale changes across the North Atlantic including in heat content, heat transport and the circulation. However there are still significant gaps in the observational coverage. Ocean reanalyses integrate the observations with a dynamically consistent ocean model and can be used to understand the observed changes. However the ability of the reanalyses to represent the dynamics must also be assessed.\ud \ud We use an ensemble of global ocean reanalyses to examine the time mean state and interannual‐decadal variability of the North Atlantic ocean since 1993. We assess how well the reanalyses are able to capture processes and whether any understanding can be gained. In particular we examine aspects of the circulation including convection, AMOC and gyre strengths, and transports. We find that reanalyses show some consistency, in particular showing a weakening of the subpolar gyre and AMOC at 50oN from the mid‐90s until at least 2009 (related to decadal variability in previous studies), a strengthening and then weakening of the AMOC at 26.5oN since 2000, and impacts of circulation changes on transports. These results agree with model studies and the AMOC observations at 26.5oN since 2005. We also see less spread across the ensemble in AMOC strength and mixed layer depth, suggesting improvements as the observational coverage has improved.

Ocean reanalyses combine ocean models, atmospheric forcing fluxes, and observations using data assimilation to give a four-dimensional description of the ocean. Metrics assessing their reliability have improved over time, allowing reanalyses to become an important tool in climate services that provide a more complete picture of the changing ocean to end users. Besides climate monitoring and research, ocean reanalyses are used to initialize sub-seasonal to multi-annual predictions, to support observational network monitoring, and to evaluate climate model simulations. These applications demand robust uncertainty estimates and fit-for-purpose assessments, achievable through sustained advances in data assimilation and coordinated inter-comparison activities. Ocean reanalyses face specific challenges: (i) dealing with intermittent or discontinued observing networks, (ii) reproducing inter-annual variability and trends of integrated diagnostics for climate monitoring, (iii) accounting for drift and bias due, e.g., to air-sea flux or ocean mixing errors, and (iv) optimizing initialization and improving performances during periods and in regions with sparse data. Other challenges such as multi-scale data assimilation to reconcile mesoscale and large-scale variability and flow-dependent error characterization for rapidly evolving processes, are amplified in long-term reanalyses. The demand to extend reanalyses backward in time requires tackling all these challenges, especially in the emerging context of earth system reanalyses and coupled data assimilation. This mini-review aims at documenting recent advances from the ocean reanalysis community, discussing unsolved challenges that require sustained activities for maximizing the utility of ocean observations, supporting data rescue and advancing specific research and development requirements for reanalyses.

Oceanic reanalyses are powerful products to reconstruct the historical 3D-state of the ocean and related circulation. At present a challenge is to have oceanic reanalyses covering the whole 20th century. This study describes the exercise of comparing available datasets to force Mediterranean Sea and global oceanic reanalyses from 1901 to present. In particular, we compared available atmospheric reanalyses with a set of experiments performed with an atmospheric general circulation model where sea surface temperature (SST) and sea-ice concentration are prescribed. These types of experiments have the advantage of covering long tune records, at least for the period for which global SST is available. and they can be performed at relatively high horizontal resolutions, a very important requisite for regional oceanic reanalyses. However, they are limited by the intrinsic model biases in representing the mean atmospheric state and its variability. In this study, we show that, within some limits, the atmospheric model performance in representing the basic variables needed for the bulk-formulae to force oceanic data assimilation systems can be comparable to the differences among available atmospheric reanalyses. In the case of the Mediterranean Sea the high horizontal resolution of the set of SST-prescribed experiments combined with their good performance in representing the surface winds in the area made them the most appropriate atmospheric forcing. On the other hand, in the case of the global ocean, atmospheric reanalyses have been proven to be still preferable due to the better representation of spatial and temporal variability of surface winds and radiative fluxes. Because of their intrinsic limitations AMIP experiments cannot provide atmospheric fields alternative to atmospheric reanalyses. Nevertheless, here we show how in the specific case of the Mediterranean Sea, they can be of use, if not preferable, to available atmospheric reanalyses.

Ocean-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.

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.

This paper analyzes the relationship between deep sedimentary fluxes and ocean current vertical velocities in an offshore area of the Ionian Sea, the deepest basin of the Eastern Mediterranean Sea. Sediment trap data are collected at 500m and 2800m depth in two successive moorings covering the period September 1999–May 2001. A tight coupling is observed between the upper and deep traps and the estimated particle sinking rates are more than 200mday−1. The current vertical velocity field is computed from a 1/16 ×1/16 Ocean General Circulation Model simulation and from the wind stress curl. Current vertical velocities are larger and more variable than Ekman vertical velocities, yet the general patterns are alike. Current vertical velocities are generally smaller than 1mday−1: we therefore exclude a direct effect of downward velocities in determining high sedimentation rates. However we find that upward velocities in the subsurface layers of the water column are positively correlated with deep particle fluxes. We thus hypothesize that upwelling would produce an increase in upper ocean nutrient levels – thus stimulating primary production and grazing – a few weeks before an enhanced vertical flux is found in the sediment traps. High particle sedimentation rates may be attained by means of rapidly sinking fecal pellets produced by gelatinous macro-zooplankton. Other sedimentation mechanisms, such as dust deposition, are also considered in explaining large pulses of deep particle fluxes. The fast sinking rates estimated in this study might be an evidence of the efficiency of the biological pump in sequestering organic carbon from the surface layers of the deep Eastern Mediterranean basins

Spurred by the sustained operation and new development of satellite and in-situ observing systems, global ocean state estimation efforts that gear towards climate applications have flourished in the past decade. A hierarchy of estimation methods is being used to routinely synthesize various observations with global ocean models. Many of the estimation products are available through public data servers. There have been an increasingly large number of applications of these products for a wide range of research topics in physical oceanography as well as other disciplines. These studies often provide important feedback to the observing systems. This white paper describes the approaches used by the estimation systems in synthesizing observations and model dynamics, highlights the applications of their products for climate research, and addresses the challenges ahead in relation to the observing systems. Further applications to study climate variability using ENSEMBLE state estimation products are described by a white paper by Stammer et al. (2010).

Histone H1 in the parasitic protozoan Leishmania is a developmentally regulated protein encoded by two genes, HIS-1.1 and HIS-1.2. These genes are separated by ∼20 kb of sequence and are located on the same DNA strand of chromosome 27. When Northern blots of parasite RNA were probed with HIS-1 strand-specific riboprobes, we detected sense and antisense transcripts that were polyadenylated and developmentally regulated. When the HIS-1.2 coding region was replaced with the coding region of the neomycin phosphotransferase gene, antisense transcription of this gene was unaffected, indicating that the regulatory elements controlling antisense transcription were located outside of the HIS-1.2 gene, and that transcription in Leishmania can occur from both DNA strands even in the presence of transcription of a selectable marker in the complementary strand. A search for other antisense transcripts within the HIS-1 locus identified an additional transcript (SC-1) within the intervening HIS-1 sequence, downstream of adenine and thymine-rich sequences. These results show that gene expression in Leishmania is not only regulated polycistronically from the sense strand of genomic DNA, but that the complementary strand of DNA also contains sequences that could drive expression of open reading frames from the antisense strand of DNA. These findings suggest that the parasite has evolved in such a way as to maximise the transcription of its genome, a mechanism that might be important for it to maintain virulence. © 2003 Australian Society for Parasitology Inc. Published by Elsevier Science Ltd. All rights reserved.

We present here a two year temperature data assimilation experiment done with a global ocean model and the latest release of the World Ocean Atlas temperature profiles. Comparison between assimilation and simulation experiments confirm the beneficial impact of the assimilation of temperature data on the overall structure of the thermocline at the Equator.