A Simple Test of the Initialization of Gravity Modes

Ballish, Bradley Alan

doi:10.1175/1520-0493(1981)109<1318:astoti>2.0.co;2

Cited by 27 publications

(3 citation statements)

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“…Filtering variability from the nonnative reanalysis that cannot be predicted by the MiKlip prediction system is implemented in FAI by projecting ocean reanalysis anomalies onto the modes of variability inherent to the prediction system. Similar methodologies have been implemented to eliminate the effect of higher frequency components (noise) on the numerical weather forecast skill, to obtain a correctly balanced initial state for data assimilation procedures, and to initialize long‐lived stable modes for seasonal predictions (e.g., Ballish, ; Branstator et al, ; Williamson, ). To this end, FAI is implemented as follows: …”

Section: Methodsmentioning

confidence: 99%

Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods

Polkova

Brune

Kadow

et al. 2019

J Adv Model Earth Syst

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Five initialization and ensemble generation methods are investigated with respect to their impact on the prediction skill of the German decadal prediction system "Mittelfristige Klimaprognose" (MiKlip). Among the tested methods, three tackle aspects of model-consistent initialization using the ensemble Kalman filter, the filtered anomaly initialization, and the initialization method by partially coupled spin-up (MODINI). The remaining two methods alter the ensemble generation: the ensemble dispersion filter corrects each ensemble member with the ensemble mean during model integration. And the bred vectors perturb the climate state using the fastest growing modes. The new methods are compared against the latest MiKlip system in the low-resolution configuration (Preop-LR), which uses lagging the climate state by a few days for ensemble generation and nudging toward ocean and atmosphere reanalyses for initialization. Results show that the tested methods provide an added value for the prediction skill as compared to Preop-LR in that they improve prediction skill over the eastern and central Pacific and different regions in the North Atlantic Ocean. In this respect, the ensemble Kalman filter and filtered anomaly initialization show the most distinct improvements over Preop-LR for surface temperatures and upper ocean heat content, followed by the bred vectors, the ensemble dispersion filter, and MODINI. However, no single method exists that is superior to the others with respect to all metrics considered. In particular, all methods affect the Atlantic Meridional Overturning Circulation in different ways, both with respect to the basin-wide long-term mean and variability and with respect to the temporal evolution at the 26 • N latitude.

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Section: Methodsmentioning

confidence: 99%

Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods

Polkova

Brune

Kadow

et al. 2019

J Adv Model Earth Syst

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“…The idea that the initial state contains predictable and nonpredictable components, and that filtering out non-predictable ones can yield more long-lasting skill, was previously tested in the context of numerical weather predictions. Along these lines, different filtering approaches were applied to initial conditions to improve forecasts by minimizing noise from the internal-gravity waves (Williamson 1976;Ballish 1981) and remove random components from initial conditions, which limit predictability, and retain those that are potentially more predictable (Branstator et al 1993). In the context of seasonal El-Niño Southern Oscillation (ENSO) predictions, an idea of initialization of coupled climate modes of variability was tested, where observed coupled modes of variability were remapped onto modeled ones (Hurrell et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Climate-mode initialization for decadal climate predictions

2019

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In the context of decadal climate predictions, a climate-mode initialization method is being tested by which ocean ORAS4 reanalysis is projected onto dominant modes of variability of the Earth System Model from the Max Planck Institute for Meteorology (MPI-ESM). The method aims to improve the prediction skill of the model by filtering out dynamically unbalanced noise during the initialization step. Used climate modes are calculated as statistical 3-D modes based on the bivariate empirical orthogonal function (EOF) analysis applied to temperature and salinity anomalies from an ensemble of historical simulations from the MPI-ESM. The climate-mode initialization method shows improved surface temperature skill, particularly over the tropical Pacific Ocean at seasonal-to-interannual timescales associated with the improved zonal momentum balance. There, the new initialization somewhat outperforms the surface temperature skill of the anomaly initialization also for lead years 2-5. In other parts of the world ocean, both initialization methods currently are equivalent in skill. However, only 44% of variance in the original ORAS4 reconstruction remains after the projection on model modes, suggesting that the ORAS4 modes are not fully compatible with the model modes. Moreover, we cannot dismiss the possibility that model modes are not sufficiently sampled with the data set underlying the EOF analysis. The full potential of the climate-mode initialization method for future decadal prediction systems therefore still needs to be quantified based on improved modal representation.

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“…The discussions about the reasons behind this problem were centered about two possibilities: convergence properties of the applied iterative algorithm and mathematical nature of the balance equations (Daley 1981;Ballish 1981;Errico 1983). The first hypothesis suggests that the appropriate choice of iterative procedure would solve the problem.…”

Section: Introductionmentioning

confidence: 99%

A Quasi-Variational Algorithm for Nonlinear Normal-Mode Initialization

Bourchtein

2010

Monthly Weather Review

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Balance equations of normal-mode initialization are nonlinear time-independent partial differential equations solved by iterative methods. For the given geopotential, there are regions where these equations are not elliptic, which is reflected in the divergence of iterative algorithms. Variational approaches used to minimize the geopotential changes are more expensive than conventional methods. In this study a simple quasi-variational algorithm is proposed based on different forms of normal-mode initialization equations, which achieves a good balance of atmospheric fields and ensures small changes of geopotential analysis values.

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A Simple Test of the Initialization of Gravity Modes

Cited by 27 publications

References 0 publications

Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods

Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods

Climate-mode initialization for decadal climate predictions

A Quasi-Variational Algorithm for Nonlinear Normal-Mode Initialization

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