Assessment of a regional physical-biogeochemical stochastic ocean model. Part 2: Empirical consistency

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordIn this Part 2 article of a two-part series, observations based on satellite missions were used to evaluate the empirical consistency of model ensembles generated via stochastic modelling of ocean physics and biogeochemistry. A high-resolution Bay of Biscay configuration was used as a case study to explore the model error subspace in both the open and coastal ocean. In Part 1 of this work, three experiments were carried out to generate model ensembles by perturbing only physics, only biogeochemistry, and both of them simultaneously. In Part 2 of this work, empirical consistency was checked, first by means of rank histograms projecting the data onto the model ensemble classes, and second, by pattern-selective consistency criteria in the space of “array modes” defined as eigenvectors of the representer matrix. Rank histograms showed large dependency on geographical region and on season for sea surface temperature (SST), sea-level anomaly (SLA), and phytoplankton functional types (PFT), shifting from consistent model-data configurations to large biases because of model ensemble underspread. Consistency for SST array modes was found to be verified at large, small and coastal scales soon after the ensemble spin-up. Array modes for the along-track sea-level showed useful consistent information at large scales and at the mesoscale; for the gridded SLA was verified only at large scale. Array modes showed that biogeochemical model uncertainties generated by stochastic physics, were effectively detected by PFT measurements at large scales, as well as at mesoscale and small-scale. By contrast, perturbing only biogeochemistry, with an identical physical forcing across the ensemble, limits the potential of PFT measurements at detecting and possibly correcting small-scale biogeochemical model errors. When an ensemble was found to be inconsistent with observations along a particular direction (here, an array mode), a plausible reason is that other error processes must have been active in the model, in addition to the ones at work across the ensemble.Centre National de la Recherche Scientifique (CNRS)European Unio

Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level

A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.RP was funded by NASA grant NNH16CT00C. CD was supported by the Australian Research Council (FT130101532 and DP 160103130), the Scientific Committee on Oceanic Research (SCOR) Working Group 148, funded by national SCOR committees and a grant to SCOR from the U.S. National Science Foundation (Grant OCE-1546580), and the Intergovernmental Oceanographic Commission of UNESCO/International Oceanographic Data and Information Exchange (IOC/IODE) IQuOD Steering Group. SJ was supported by the Natural Environmental Research Council under Grant Agreement No. NE/P01517/1 and by the EPSRC NEWTON Fund Sustainable Deltas Programme, Grant Number EP/R024537/1. RvdW received funding from NWO, Grant 866.13.001. WH was supported by NASA (NNX17AI63G and NNX17AH25G). CL was supported by NASA Grant NNH16CT01C. This work is a contribution to the PIRATE project funded by CNES (to TP). PT was supported by the NOAA Research Global Ocean Monitoring and Observing Program through its sponsorship of UHSLC (NA16NMF4320058). JS was supported by EU contract 730030 (call H2020-EO-2016, “CEASELESS”). JW was supported by EU Horizon 2020 Grant 633211, Atlantos

Ensemble downscaling of a regional ocean model

We downscaled a free ensemble of a regional, parent model to a high-resolution coastal, child ensemble in the Bay of Biscay. The child ensemble was forced at the open boundaries by the parent ensemble, and locally by perturbing the winds. By comparing ensembles generated by each of these forcing perturbations separately and combined we were able to consider the ensemble from either of two paradigms: (1) characterising high-resolution, coastal model errors using local and non-local forcing perturbations, or (2) downscaling regional model errors into the coastal domain. We found that most of the spread in the child ensembles was generated from the ensemble of open boundary conditions, with the local wind perturbations on their own generating substantially less ensemble spread. Together, the two sources of error increased the ensemble spread by only a small amount over the non-local perturbations alone. In general, the spread in sea surface height was greater in the child ensembles than in the parent ensemble, probably due to the more refined dynamics, while the spread in sea surface temperature was lower, likely due to the way the open boundary conditions were averaged. Deep below the surface, though, the child ensemble featured a large spread even where the parent model's spread was very weak. This enhanced error response is a promising result for an ensemble data assimilation system, as it could be exploited to correct the model deep below the surface. © 2019 Elsevier Lt

Assessment of a regional physical–biogeochemical stochastic ocean model. Part 2: Empirical consistency

In this Part 2 article of a two-part series, observations based on satellite missions were used to evaluate the empirical consistency of model ensembles generated via stochastic modelling of ocean physics and biogeochemistry. A high-resolution Bay of Biscay configuration was used as a case study to explore the model error subspace in both the open and coastal ocean. In Part 1 of this work, three experiments were carried out to generate model ensembles by perturbing only physics, only biogeochemistry, and both of them simultaneously. In Part 2 of this work, empirical consistency was checked, first by means of rank histograms projecting the data onto the model ensemble classes, and second, by pattern-selective consistency criteria in the space of “array modes” defined as eigenvectors of the representer matrix. Rank histograms showed large dependency on geographical region and on season for sea surface temperature (SST), sea-level anomaly (SLA), and phytoplankton functional types (PFT), shifting from consistent model-data configurations to large biases because of model ensemble underspread. Consistency for SST array modes was found to be verified at large, small and coastal scales soon after the ensemble spin-up. Array modes for the along-track sea-level showed useful consistent information at large scales and at the mesoscale; for the gridded SLA was verified only at large scale. Array modes showed that biogeochemical model uncertainties generated by stochastic physics, were effectively detected by PFT measurements at large scales, as well as at mesoscale and small-scale. By contrast, perturbing only biogeochemistry, with an identical physical forcing across the ensemble, limits the potential of PFT measurements at detecting and possibly correcting small-scale biogeochemical model errors. When an ensemble was found to be inconsistent with observations along a particular direction (here, an array mode), a plausible reason is that other error processes must have been active in the model, in addition to the ones at work across the ensemble. © 2021 Elsevier Lt

Synergies en océanographie opérationnelle: le besoin intrinsèque et permanent d'observations océaniques

International audienceOperational oceanography can be described as the provision of routine oceanographic information needed for decision-making purposes. It is dependent upon sustained research and development through the end-to-end framework of an operational service, from observation collection to delivery mechanisms. The core components of operational oceanographic systems are a multi-platform observation network, a data management system, a data assimilative prediction system, and a dissemination/accessibility system. These are interdependent, necessitating communication and exchange between them, and together provide the mechanism through which a clear picture of ocean conditions, in the past, present, and future, can be seen. Ocean observations play a critical role in all aspects of operational oceanography, not only for assimilation but as part of the research cycle, an

Towards Comprehensive Observing and Modeling Systems for Monitoring and Predicting Regional to Coastal Sea Level

A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change