The Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0

Cruz, Lesley De; Demaeyer, Jonathan; Vannitsem, Stéphane

doi:10.5194/gmd-9-2793-2016

Cited by 51 publications

(113 citation statements)

References 35 publications

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“…This question is therefore difficult to answer using observational data sets, and one must rely on the dynamics generated in coupled models of increasing complexity, as suggested in Ghil [, and references therein]. Vannitsem et al [] have started such an analysis, and it will be extended to more complicated coupled systems, like the one recently developed by De Cruz et al [].…”

Section: Discussionmentioning

confidence: 99%

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Evidence of coupling in ocean‐atmosphere dynamics over the North Atlantic

Vannitsem

Ghil

2017

Geophysical Research Letters

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Coupling between the ocean and the atmosphere is investigated in reanalysis data sets. Projecting the data sets onto a dynamically defined subspace allows one to isolate the dominant modes of variability of the coupled system. This coupled projection is then analyzed using multichannel singular spectrum analysis. The results suggest that a dominant low‐frequency signal with a 25–30 year period already mentioned in the literature is a common mode of variability of the atmosphere and the ocean. A new score for evaluating the internal nature of the common variability is then introduced, and it confirms the presence of coupled dynamics in the ocean‐atmosphere system that impacts the atmosphere at large scale. The physical nature of this coupled dynamics is then discussed.

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Section: Discussionmentioning

confidence: 99%

“…In order to investigate the dynamics of the coupled system over the North Atlantic, we use reanalysis data sets from the ECMWF and project them onto Fourier modes that dominate the coupled ocean‐atmosphere dynamics in a low‐order coupled model [ Vannitsem et al , ; Vannitsem , ; De Cruz et al , ].…”

Section: Data Setsmentioning

confidence: 99%

Evidence of coupling in ocean‐atmosphere dynamics over the North Atlantic

Vannitsem

Ghil

2017

Geophysical Research Letters

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“…The integration time step affects the numerical stability of the analysis‐forecast cycle. We choose integration time steps either dt = 0.01 (as De Cruz et al, ), 0.1, or 0.5 nondimensional model time units (MTUs). The shorter time step facilitates varying the coupling frequency, which we use in the investigation of the Lyapunov exponents (LEs) computed from the coupled model.…”

Section: Methodsmentioning

confidence: 99%

“…We use the MAOOAM three-layer (two-layer atmosphere and one-layer ocean) truncated QG model developed by De Cruz et al (2016). Our experiments use the configuration examined by Vannitsem and Lucarini (2016), namely, the 2 × 2 atmosphere and 2 × 4 ocean (referring to the level of spectral truncation).…”

Section: Model Configurationmentioning

confidence: 99%

“…All demonstrations are carried out with the simplified Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM) developed by De Cruz et al (2016). The atmospheric component of the MAOOAM is based on Charney and Straus (1980), Cehelsky and Tung (1987) and Reinhold and Pierrehumbert (1982), while the ocean component is based on Pierini (2011) and Barsugli and Battisti (1998).…”

Section: Introductionmentioning

confidence: 99%

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Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model

Penny

Bach

Bhargava

et al. 2019

J Adv Model Earth Syst

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Strongly coupled data assimilation (SCDA) views the Earth as one unified system. This allows observations to have an instantaneous impact across boundaries such as the air‐sea interface when estimating the state of each individual component. Operational prediction centers are moving toward Earth system modeling for all forecast timescales, ranging from days to months. However, there have been few studies that examine fundamental aspects of SCDA and the transition from traditional approaches that apply data assimilation only to a single component, whether forecasts were derived from a coupled model or an uncoupled forced model. The SCDA approach is examined here in detail using numerical experiments with a simple coupled atmosphere‐ocean quasi‐geostrophic model. The impact of coupling is explored with respect to its impact on the Lyapunov spectrum and on data assimilation system stability. Different data assimilation methods are compared within the context of SCDA, including the 3‐D and 4‐D Variational methods, the ensemble Kalman filter, and the hybrid gain method. The impact of observing system coverage is also investigated. We find that SCDA is generally superior to weakly coupled or uncoupled approaches. Dynamically defined background error covariance estimates are essential for SCDA to achieve an accurate coupled state estimate as the observing system becomes sparser. As a clarification of seemingly contradictory findings from previous studies, it is shown that ocean observations can adequately constrain atmospheric state estimates provided that the analysis‐observing frequency is sufficiently high and the ensemble size determining the background error covariance is sufficiently large.

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