ASSESSING THE REALISM OF OCEAN SIMULATIONS AGAINST HYDROGRAPHY AND ALTIMETRY

Future climate prediction systems will include ocean models at eddy-admitting to eddy-resolving resolution, i.e. ° on the horizontal or finer. The development and calibration of such models requires the us

Can we map the interannual variability of the whole upper Southern Ocean with the current database of hydrographic observations?

International audienceWith the advent of Argo floats, it now seems feasible to study the interannual variations of upper ocean hydrographic properties of the historically undersampled Southern Ocean. To do so, scattered hydrographic profiles often first need to be mapped. To investigate biases and errors associated both with the limited space-time distribution of the profiles and with the mapping methods, we colocate the mixed-layer depth (MLD) output from a state-of-the-art 1/12° DRAKKAR simulation onto the latitude, longitude, and date of actual in situ profiles from 2005 to 2014. We compare the results obtained after remapping using a nearest neighbor (NN) interpolation and an objective analysis (OA) with different spatiotemporal grid resolutions and decorrelation scales. NN is improved with a coarser resolution. OA performs best with low decorrelation scales, avoiding too strong a smoothing, but returns values over larger areas with large decorrelation scales and low temporal resolution, as more points are available. For all resolutions OA represents better the annual extreme values than NN. Both methods underestimate the seasonal cycle in MLD. MLD biases are lower than 10 m on average but can exceed 250 m locally in winter. We argue that current Argo data should not be mapped to infer decadal trends in MLD, as all methods are unable to reproduce existing trends without creating unrealistic extra ones. We also show that regions of the subtropical Atlantic, Indian, and Pacific Oceans, and the whole ice-covered Southern Ocean, still cannot be mapped even by the best method because of the lack of observational data

Intrinsic variability of the Atlantic Meridional Overturning Circulation at interannual-to-multidecadal time scales

The low-frequency variability of the Atlantic meridional overturning circulation (AMOC) is investigated from 2, ¼°, and ° global ocean–sea ice simulations, with a specific focus on its internally generated (i.e., “intrinsic”) component. A 327-yr climatological ¼° simulation, driven by a repeated seasonal cycle (i.e., a forcing devoid of interannual time scales), is shown to spontaneously generate a significant fraction R of the interannual-to-decadal AMOC variance obtained in a 50-yr “fully forced” hindcast (with reanalyzed atmospheric forcing including interannual time scales). This intrinsic variance fraction R slightly depends on whether AMOCs are computed in geopotential or density coordinates, and on the period considered in the climatological simulation, but the following features are quite robust when mesoscale eddies are simulated (at both ¼° and ° resolutions); R barely exceeds 5%–10% in the subpolar gyre but reaches 30%–50% at 34°S, up to 20%–40% near 25°N, and 40%–60% near the Gulf Stream. About 25% of the meridional heat transport interannual variability is attributed to intrinsic processes at 34°S and near the Gulf Stream. Fourier and wavelet spectra, built from the 327-yr ¼° climatological simulation, further indicate that spectral peaks of intrinsic AMOC variability (i) are found at specific frequencies ranging from interannual to multidecadal, (ii) often extend over the whole meridional scale of gyres, (iii) stochastically change throughout these 327 yr, and (iv) sometimes match the spectral peaks found in the fully forced hindcast in the North Atlantic. Intrinsic AMOC variability is also detected at multidecadal time scales, with a marked meridional coherence between 35°S and 25°N (15–30 yr periods) and throughout the whole basin (50–90-yr periods)

Stochastic variability of oceanic flows above topography anomalies

International audienceWe describe a stochastic variability mechanism which is genuinely internal to the ocean, i.e. not due to fluctuations in atmospheric forcing. % The key ingredient is the existence of closed contours of bottom topography surrounded by a stirring region of enhanced eddy activity. This configuration leads to the formation of a robust but highly variable vortex above the topography anomaly. The vortex dynamics integrates the white noise forcing of oceanic eddies into a red noise signal for the large scale volume transport of the vortex. The strong interannual fluctuations of the transport of the Zapiola anticyclone ($\sim 100 \ Sv$) in the Argentine basin are argued to be partly due to such eddy-driven stochastic variability, on the basis of a $310$ years long simulation of a comprehensive global ocean model run driven by a repeated-year forcing

The Atlantic meridional overturning circulation in high resolution models

The Atlantic meridional overturning circulation (AMOC) represents the zonally integrated stream function of meridional volume transport in the Atlantic Basin. The AMOC plays an important role in transporting heat meridionally in the climate system. Observations suggest a heat transport by the AMOC of 1.3 PW at 26°N ‐ a latitude which is close to where the Atlantic northward heat transport is thought to reach its maximum. This shapes the climate of the North Atlantic region as we know it today. In recent years there has been significant progress both in our ability to observe the AMOC in nature and to simulate it in numerical models. Most previous modeling investigations of the AMOC and its impact on climate have relied on models with horizontal resolution that does not resolve ocean mesoscale eddies and the dynamics of the Gulf Stream/North Atlantic Current system. As a result of recent increases in computing power, models are now being run that are able to represent mesoscale ocean dynamics and the circulation features that rely on them. The aim of this review is to describe new insights into the AMOC provided by high‐resolution models. Furthermore, we will describe how high‐resolution model simulations can help resolve outstanding challenges in our understanding of the AMOC

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

Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the ‘‘Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Altimetry for the future: building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the “Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Identification and filtering of the chaotic ocean variability by Machine Learning

International audienc