Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation

Sluka, Travis; Penny, Stephen G.; Kalnay, Eugenia; Miyoshi, Takemasa

doi:10.1002/2015gl067238

Cited by 97 publications

(121 citation statements)

References 32 publications

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“…Sea-surface temperature and sea-surface salinity are relaxed to their respective monthly climatologies31. The results capture most of the prominent ocean variabilities at scales larger than intra-seasonal scales27. The ocean model is then made “imperfect” by turning off the relaxations and perturbing the initial conditions.…”

Section: Observation System Simulation Experimentsmentioning

confidence: 95%

The pre-Argo ocean reanalyses may be seriously affected by the spatial coverage of moored buoys

Sanikommu

Paul

Sluka

et al. 2017

Sci Rep

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Assimilation methods, meant to constrain divergence of model trajectory from reality using observations, do not exactly satisfy the physical laws governing the model state variables. This allows mismatches in the analysis in the vicinity of observation locations where the effect of assimilation is most prominent. These mismatches are usually mitigated either by the model dynamics in between the analysis cycles and/or by assimilation at the next analysis cycle. However, if the observations coverage is limited in space, as it was in the ocean before the Argo era, these mechanisms may be insufficient to dampen the mismatches, which we call shocks, and they may remain and grow. Here we show through controlled experiments, using real and simulated observations in two different ocean models and assimilation systems, that such shocks are generated in the ocean at the lateral boundaries of the moored buoy network. They thrive and propagate westward as Rossby waves along these boundaries. However, these shocks are essentially eliminated by the assimilation of near-homogenous global Argo distribution. These findings question the fidelity of ocean reanalysis products in the pre-Argo era. For example, a reanalysis that ignores Argo floats and assimilates only moored buoys, wrongly represents 2008 as a negative Indian Ocean Dipole year.

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Section: Observation System Simulation Experimentsmentioning

confidence: 95%

The pre-Argo ocean reanalyses may be seriously affected by the spatial coverage of moored buoys

Sanikommu

Paul

Sluka

et al. 2017

Sci Rep

Self Cite

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“…Sluka et al () used the intermediate complexity coupled SPEEDY/NEMO GCM at a higher resolution than the studies described above, both for the atmosphere at T30 and for the ocean using a 2° horizontal grid telescoping to 0.25° in the tropics. They applied CDA to assimilate atmospheric observations into the coupled system and compared the impacts of SCDA versus WCDA on the ocean state estimate.…”

Section: Related Workmentioning

confidence: 99%

“…The benefits of SCDA have been investigated in several studies using either an EnKF (Liu et al, ; Lu et al , ; Sluka et al, ) or 4D‐Var data assimilation system (Smith et al, ). We investigate the transition from the paradigm applying DA to independent forced atmosphere and ocean models to the new paradigm using a fully coupled forecast model with CDA.…”

Section: Introductionmentioning

confidence: 99%

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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“…For producing more consistent ocean‐atmosphere reanalyses, coupled general circulation models have recently started to be used in DA, with the most recent examples being the NCEP's Climate Forecast System Reanalysis (CFSR; Saha et al, , ), UK Met Office's coupled DA system (Lea et al, ) and the Coupled European Centre for Medium‐range Weather Forecasts (ECMWF) ReAnalysis system (CERA; Laloyaux et al, ). Such systems are based on coupled models with DA implemented individually for the atmosphere and ocean components and are usually named as “weakly” coupled DA systems as no cross‐medium background error covariances are directly used (Lu et al, ; Sluka et al, ). It is essential to assess the strengths and weaknesses of these coupled reanalyses with respect to uncoupled equivalents, but this can be difficult because coupled reanalyses often have different resolution or use different atmospheric models.…”

Section: Introductionmentioning

confidence: 99%

Improved SST‐Precipitation Intraseasonal Relationships in the ECMWF Coupled Climate Reanalysis

Feng

Haines

Liu

et al. 2018

Geophysical Research Letters

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The European Centre for Medium‐range Weather Forecasts (ECMWF) has produced the ocean‐atmosphere coupled reanalysis for the twentieth century, CERA‐20C, following on from the similar but atmosphere‐only reanalysis ERA‐20C. Here we demonstrate the capability of CERA‐20C in producing more physically consistent ocean and atmosphere boundary conditions, by focusing on sea surface temperature (SST)‐precipitation intraseasonal relationships. CERA‐20C reproduces well the observed SST‐precipitation correlations, while these relationships are poorly represented in ERA‐20C, with the greatest discrepancies in the early 1900s. The improved relationships in CERA‐20C are due to intraseasonal improvements in SST that are not present in the external HadISST2 product. In CERA‐20C, SST‐precipitation relationships are slightly weaker in the 1900s than in the 2000s, mainly due to differences in the assimilated observation density. We also find that the coupled model initialized from CERA‐20C in the 2000s realistically simulates these relationships, while relaxing SST toward HadISST2 tends to damp these relationships. CERA‐20C has improved mean and variance in precipitation over ERA‐20C, but these are mostly due to improvements in the atmospheric model and not due to coupled feedbacks.

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Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation

Cited by 97 publications

References 32 publications

The pre-Argo ocean reanalyses may be seriously affected by the spatial coverage of moored buoys

The pre-Argo ocean reanalyses may be seriously affected by the spatial coverage of moored buoys

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

Improved SST‐Precipitation Intraseasonal Relationships in the ECMWF Coupled Climate Reanalysis

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