Dynamical evolution of the error statistics with the SEEK filter to assimilate altimetric data in eddy-resolving ocean models

ISSN 0035 - 9009International audienceThe Singular Evolutive Extended Kalman (SEEK) filter introduced by Pham et al. is applied to a primitive‐equation model in order to reconstruct the mesoscale circulation typical of the mid‐latitude ocean from altimetric data. The SEEK filter is a variant of the Kalman‐filter algorithm based on two concepts: the order reduction of the initial‐error covariance matrix, and the dynamical evolution of the reduced‐order basis. This makes the method potentially suitable for problems with a high number of degrees of freedom.Previous work has shown the ability of a steady version of the filter to improve the vertical structure of the ocean thermocline in the case of the quasi‐linear dynamics associated with the equatorial tropical Pacific Ocean, and the need to combine the dynamical evolution of the basis with an adaptive scheme in a mid‐latitude ocean model of the Gulf Stream region.This work examines the potential advantages of the dynamical evolution of the basis functions with simple assimilation experiments. It demonstrates the ability of the method to propagate in time the statistical properties of the system when the filter is initialized properly. However, the lack of robustness of the filter is investigated theoretically and experimentally, showing the need to consider variants of the method when the filter is not properly initialized

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

Here we assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. The baseline experiment assimilates satellite sea level (SL), sea surface temperature (SST), and in situ subsurface temperature and salinity observations (Tz, Sz). These baseline experiments are then compared with experiments that additionally assimilate Aquarius (version 5.0 Lilly and Lagerloef, 2008) and SMAP (version 2.0 Meissner and Wentz, 2016) SSS. Twelve-month forecasts are initialized for each month from September 2011 to September 2017. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation over 0-8 month forecast lead-times and removing the salty bias from SMAP data helps to extend useful forecasts out to 12 month lead-times

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO 3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10 degrees South latitude-5 degrees North latitude. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO 3.4 region. In addition, salting near the ITCZ (Intertropical Convergence Zone) leads to a deepening of the mixed layer and thermocline near 8 degrees North latitude. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

We assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. Our coupled model is composed of a primitive equation ocean model for the tropical Indo-Pacific region that is coupled with the global SPEEDY atmospheric model (Molteni, 2003). The Ensemble Reduced Order Kalman Filter is used to assimilate observations to constrain dynamics and thermodynamics for initialization of the coupled model. The baseline experiment assimilates satellite sea level, SST, and in situ subsurface temperature and salinity observations. This baseline is then compared with experiments that additionally assimilate Aquarius (version 4.0) and SMAP (version 2.0) SSS. Twelve-month forecasts are initialized for each month from Sep. 2011 to Dec. 2016. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation after 1 out to 12 month forecast lead times. For initialization of the coupled forecast, the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10S-5N. SST differences at initialization force wide-spread downwelling favorable curl over most of the tropical Pacific. Over an average forecast, SST remains warmer with SSS assimilation at the eastern edge of the warm pool. This warm SST propagates into the eastern Pacific and drags westerly wind anomalies eastward into the NINO3.4 region. In addition, salting near the ITCZ leads to a deepening of the mixed layer and thermocline near 8N. These patterns together lead to a funneling effect that provides the background state to amplify equatorial Kelvin waves. We show that the downwelling Kelvin waves are amplified by assimilating satellite SSS and lead to significantly improved forecasts particularly for the 2015 El Nino

Model initialization in a tidally energetic regime : a dynamically adjusted objective analysis

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Ocean Modelling 36 (2011): 219-227, doi:10.1016/j.ocemod.2011.01.001.A simple improvement to objective analysis of hydrographic data is proposed to eliminate spatial aliasing e ects in tidally energetic regions. The proposed method consists of the evaluation of anomalies from observations with respect to circulation model elds. The procedure is run iteratively to achieve convergence. The method is applied in the Bay of Fundy and compared with traditional objective analysis procedures and dynamically adjusted climatological elds. The hydrographic skill (di erence between observed and model temperature and salinity) of the dynamically adjusted objective analysis is signi cantly improved by reducing bias and correcting the vertical structure. Representation of the observed velocities is also improved. The resulting ow is consistent with the known circulation in the Bay.The preparation of this paper was supported by NSF/NIEHS grant OCE- 0430724 (Woods Hole Center for Oceans and Human Health) and NOAA grant NA06NOS4780245 (GOMTOX)

Analysis of relevant technical issues and deficiencies of the existing sensors and related initiatives currently set and working in marine environment. New generation technologies for cost-effective sensors

The last decade has seen significant growth in the field of sensor networks, which are currently collecting large amounts of environmental data. This data needs to be collected, processed, stored and made available for analysis and interpretation in a manner which is meaningful and accessible to end users and stakeholders with a range of requirements, including government agencies, environmental agencies, the research community, industry users and the public. The COMMONSENSE project aims to develop and provide cost-effective, multi-functional innovative sensors to perform reliable in-situ measurements in the marine environment. The sensors will be easily usable across several platforms, and will focus on key parameters including eutrophication, heavy metal contaminants, marine litter (microplastics) and underwater noise descriptors of the MSFD. The aims of Tasks 2.1 and 2.2 which comprise the work of this deliverable are: • To obtain a comprehensive understanding and an up-to-date state of the art of existing sensors. • To provide a working basis on “new generation” technologies in order to develop cost-effective sensors suitable for large-scale production. This deliverable will consist of an analysis of state-of-the-art solutions for the different sensors and data platforms related with COMMONSENSE project. An analysis of relevant technical issues and deficiencies of existing sensors and related initiatives currently set and working in marine environment will be performed. Existing solutions will be studied to determine the main limitations to be considered during novel sensor developments in further WP’s. Objectives & Rationale The objectives of deliverable 2.1 are: • To create a solid and robust basis for finding cheaper and innovative ways of gathering data. This is preparatory for the activities in other WPs: for WP4 (Transversal Sensor development and Sensor Integration), for WP(5-8) (Novel Sensors) to develop cost-effective sensors suitable for large-scale production, reducing costs of data collection (compared to commercially available sensors), increasing data access availability for WP9 (Field testing) when the deployment of new sensors will be drawn and then realized

An assessment of upper ocean salinity content from the ocean reanalyses inter-comparison project (ORA-IP)

Many institutions worldwide have developed ocean reanalyses systems (ORAs) utilizing a variety of ocean models and assimilation techniques. However, the quality of salinity reanalyses arising from the various ORAs has not yet been comprehensively assessed. In this study, we assess the upper ocean salinity content (depth-averaged over 0–700 m) from 14 ORAs and 3 objective ocean analysis systems (OOAs) as part of the Ocean Reanalyses Intercomparison Project. Our results show that the best agreement between estimates of salinity from different ORAs is obtained in the tropical Pacific, likely due to relatively abundant atmospheric and oceanic observations in this region. The largest disagreement in salinity reanalyses is in the Southern Ocean along the Antarctic circumpolar current as a consequence of the sparseness of both atmospheric and oceanic observations in this region. The West Pacific warm pool is the largest region where the signal to noise ratio of reanalysed salinity anomalies is >1. Therefore, the current salinity reanalyses in the tropical Pacific Ocean may be more reliable than those in the Southern Ocean and regions along the western boundary currents. Moreover, we found that the assimilation of salinity in ocean regions with relatively strong ocean fronts is still a common problem as seen in most ORAs. The impact of the Argo data on the salinity reanalyses is visible, especially within the upper 500m, where the interannual variability is large. The increasing trend in global-averaged salinity anomalies can only be found within the top 0–300m layer, but with quite large diversity among different ORAs. Beneath the 300m depth, the global-averaged salinity anomalies from most ORAs switch their trends from a slightly growing trend before 2002 to a decreasing trend after 2002. The rapid switch in the trend is most likely an artefact of the dramatic change in the observing system due to the implementation of Argo

Impact of Aquarius sea surface salinity observations on coupled forecasts for the tropical Indo-Pacific Ocean

23 pages, 12 figures, 1 tableThis study demonstrates the impact of gridded in situ and Aquarius sea surface salinity (SSS) on coupled forecasts for August 2011 until February 2014. Assimilation of all available subsurface temperature (ASSIM-Tz) is chosen as the baseline and an optimal interpolation of all in situ salinity (ASSIM-Tz-SSSIS) and Aquarius SSS (ASSIM-T z-SSSAQ) are added in separate assimilation experiments. These three are then used to initialize coupled experiments. Including SSS generally improves NINO3 sea surface temperature anomaly validation. For ASSIM-Tz-SSSIS, correlation is improved after 7 months, but the root mean square error is degraded with respect to ASSIM-Tz after 5 months. On the other hand, assimilating Aquarius gives significant improvement versus ASSIM-Tz for all forecast lead times after 5 months. Analysis of the initialization differences with the baseline indicates that SSS assimilation results in an upwelling Rossby wave near the dateline. In the coupled model, this upwelling signal reflects at the western boundary eventually cooling the NINO3 region. For this period, coupled models tend to erroneously predict NINO3 warming, so SSS assimilation corrects this defect. Aquarius is more efficient at cooling the NINO3 region since it is relatively more salty in the eastern Pacific than in situ SSS which leads to increased mixing and upwelling which in turn sets up enhanced west-to-east SST gradient and intensified Bjerknes coupling. A final experiment that uses subsampled Aquarius at in situ locations infers that high-density spatial sampling of Aquarius is the reason for the superior performance of Aquarius versus in situ SSS. Key Points Assimilation of sea surface salinity (SSS) improves coupled forecasts Aquarius outperforms in situ SSS assimilation SSS assimilation imparts a relative improved upwelling signal © 2014. American Geophysical Union. All Rights ReservedThis research is supported by NASA Physical Oceanography grant NNX12AN08G and the Ocean Salinity Science Team (NNX09AU74G). Ballabrera-Poy was supported by the MIDAS-7 AYA2012-39356-C05-03 Spanish grantPeer Reviewe

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

Special issue Ten Years of Remote Sensing at Barcelona Expert Center.-- 21 pages, 12 figures, 2 tablesSoil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth's surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010-June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global assessment of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observationsPeer Reviewe