On the Benefit of Current and Future ALPS Data for Improving Arctic Coupled Ocean-Sea Ice State Estimation

Nguyen, Anhco; Ocaña, Victor; Garg, Vikram; Heimbach, Patrick; Toole, John M.; Krishfield, Richard A.; Lee, Craig; Rainville, Luc

doi:10.5670/oceanog.2017.223

Cited by 20 publications

(27 citation statements)

References 41 publications

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“…In contrast to both OSEs and OSSEs, where statistical approach is an important component of the DA step, adjointbased methods utilize the dynamical information in the tangent linear and adjoint models of the underlying general circulation model (GCM). Through the equations which capture conservation and constitutive laws, propagation of information up-and down-stream of any quantity of interest (QoI) is used to (a) assess impactful regions where new observations can be potentially deployed (Marotzke et al, 1999;Zanna et al, 2010;Heimbach et al, 2011;Nguyen et al, 2017;Stammer et al, 2018); (b) assess the redundancy of existing observing networks (Köhl and Stammer, 2004;Moore et al, 2017b); (c) quantify the impacts of selected existing/new observational networks on reducing posterior uncertainties of the GCM control parameters and/or potential unobserved remote QoI (Moore et al, 2011(Moore et al, , 2017aBui-Thanh et al, 2012;Heimbach, 2014, 2018;Kaminski et al, 2015Kaminski et al, , 2018; (d) find an optimal observing network through Hessian-based OED that minimizes the posterior uncertainties as a function of the control parameters and/or targeted QoI (Alexanderian et al, 2016;Loose, 2019). The advantage of the adjoint-based methods is not only the quantification of uncertainty reduction of the GCM control parameters and/or any specific QoI to the observing network but also the identification of dynamical connection and causal relationship between them.…”

Section: System Optimizationmentioning

confidence: 99%

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

et al. 2019

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Rapid Arctic warming drives profound change in the marine environment that have significant socio-economic impacts within the Arctic and beyond, including climate and weather hazards, food security, transportation, infrastructure planning and resource extraction. These concerns drive efforts to understand and predict Arctic environmental change and motivate development of an Arctic Region Component of the Global Ocean Observing System (ARCGOOS) capable of collecting the broad, sustained observations needed to support these endeavors. This paper provides a roadmap for establishing the ARCGOOS. ARCGOOS development must be underpinned by a broadly endorsed framework grounded in high-level policy drivers and the scientific and operational objectives that stem from them. This should be guided by a transparent, internationally accepted governance structure with recognized authority and organizational relationships with the national agencies that ultimately execute network plans. A governance model for ARCGOOS must guide selection of objectives, assess performance and fitness-to-purpose, and advocate for resources. A requirements-based framework for an ARCGOOS begins with the Societal Benefit

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Section: System Optimizationmentioning

confidence: 99%

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

et al. 2019

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“…Water and air properties surrounding sea-ice are needed for seaice prediction. Currently, there are no routine observations taken near the edge of sea-ice nor in the transition marginal ice zone (MIZ) between sea ice and open water (Lee et al, 2017;Nguyen et al, 2017). Permanent moored subsurface buoys and ice-tethered profilers could be a key source of time-series data in these data sparse regions.…”

Section: Need For New Observing Technology For S2s Predictionsmentioning

confidence: 99%

Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability

et al. 2019

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These observational platforms should also be tested and evaluated in ocean observation sensitivity experiments with current and future generation CDA and S2S prediction systems. Investments in the new ocean observations as well as model and DA system developments can lead to substantial returns on cost savings from disaster mitigation as well as socio-economic decisions that use S2S forecast information.

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“…A display of fields on the native grid, including high latitudes, can be seen in the various references and on the ECCO website. A specific high-northern-latitude version of the state estimate and its corresponding climatology is in preparation (Nguyen et al 2017). Elsewhere, longitudes are uniformly spaced at 1° and latitudes telescope toward the equator (where it is 0.41°) and decrease northward from 1° to about 1/2° at 57°.…”

Section: Basic Fieldsmentioning

confidence: 99%

A Dynamically Consistent, Multivariable Ocean Climatology

Fukumori

Heimbach

Ponte

et al. 2018

Bulletin of the American Meteorological Society

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A dynamically consistent 20-yr average ocean climatology based on monthly values during the years 1994–2013 has been produced from the most recent state estimate of the Estimating the Circulation and Climate of the Ocean (ECCO) project, globally, top to bottom. The estimate was produced from a least squares fit of a free-running ocean general circulation model to almost all available near-global data. Data coverage in space and time during this period is far more homogeneous than in any earlier interval and includes CTD, elephant seal, and Argo temperature and salinity profiles; sea ice coverage; full altimetric and gravity-field coverage; satellite sea surface temperatures; and the initializing meteorological coverage from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Dominant remaining data inhomogeneity arises from increasing coverage from the Argo profiles from about 2000 to the present. The state estimate exactly satisfies the primitive equations of the free-running Massachusetts Institute of Technology General Circulation Model (MITgcm) at all times and hence produces values satisfying the fundamental conservation laws of energy, freshwater, and so forth, permitting its use for climate change studies. Quantities such as calculated heat content depend upon all observations, not just temperature, for example, altimetric height and meteorological exchanges. Output files are publicly available in Network Common Data Form (netCDF) and MATLAB form and include hydrographic variables, three components of velocity, and pressure at all depths, as well as other variables, including inferred air–sea momentum and buoyancy fluxes, 3D mixing parameters, and sea ice cover.

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On the Benefit of Current and Future ALPS Data for Improving Arctic Coupled Ocean-Sea Ice State Estimation

Cited by 20 publications

References 41 publications

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System

Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability

A Dynamically Consistent, Multivariable Ocean Climatology

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