Improvement of hydrodynamic forecasting of Danish waters: impact of low-frequency North Atlantic barotropic variations

Büchmann, Bjarne; Hansen, Carsten Jahn; Söderkvist, Johan

doi:10.1007/s10236-011-0451-2

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…In this study, the FCOO operational GETM setup 'NS1C' (Bu¨chmann et al, 2011) is examined. It is a 60-layer, 1 nautical mile resolution setup covering the North SeaÁ Baltic Sea area, see Fig.…”

Section: The Fcoo Model Setupmentioning

confidence: 99%

“…The stochastic uncertainty connected to the simulations of a single 3-D baroclinic ocean model setup has been examined, namely the FCOO NS1C operational setup, see Bu¨chmann et al (2011). The model compilation and execution are fully repeatable, so the uncertainty is unrelated to random rounding effects taking place in the floating point computations.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

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Internal variability of a 3-D ocean model

Büchmann

Söderkvist

2016

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TThe Defence Centre for Operational Oceanography runs operational forecasts for the Danish waters. The core setup is a 60-layer baroclinic circulation model based on the General Estuarine Transport Model code. At intervals, the model setup is tuned to improve 'model skill' and overall performance. It has been an area of concern that the uncertainty inherent to the stochastical/chaotic nature of the model is unknown. Thus, it is difficult to state with certainty that a particular setup is improved, even if the computed model skill increases. This issue also extends to the cases, where the model is tuned during an iterative process, where model results are fed back to improve model parameters, such as bathymetry. An ensemble of identical model setups with slightly perturbed initial conditions is examined. It is found that the initial perturbation causes the models to deviate from each other exponentially fast, causing differences of several PSUs and several kelvin within a few days of simulation. The ensemble is run for a full year, and the long-term variability of salinity and temperature is found for different regions within the modelled area. Further, the developing time scale is estimated for each region, and great regional differences are found Á in both variability and time scale. It is observed that periods with very high ensemble variability are typically short-term and spatially limited events. A particular event is examined in detail to shed light on how the ensemble 'behaves' in periods with large internal model variability. It is found that the ensemble does not seem to follow any particular stochastic distribution: both the ensemble variability (standard deviation or range) as well as the ensemble distribution within that range seem to vary with time and place. Further, it is observed that a large spatial variability due to mesoscale features does not necessarily correlate to large ensemble variability. These findings bear impact on the way data assimilation should be addressed Á especially in relation to operational forecasts.

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Section: The Fcoo Model Setupmentioning

confidence: 99%

Section: Conclusion and Further Workmentioning

confidence: 99%

Internal variability of a 3-D ocean model

Büchmann

Söderkvist

2016

Tellus A: Dynamic Meteorology and Oceanography

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“…The inner model (Figure a) provides a high‐resolution representation of the central Baltic Sea. The bathymetry was linearly interpolated from a 1 nm grid [ Büchmann et al ., ] due to the lack of higher‐resolution bathymetric data for the central Baltic Sea. At the open boundaries (Figure a), boundary conditions were derived from the large‐scale model described above.…”

Section: Study Area and Model Descriptionmentioning

confidence: 99%

Deep‐water dynamics and boundary mixing in a nontidal stratified basin: A modeling study of the Baltic Sea

et al. 2014

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Recent results from a tracer release experiment have shown that, similar to many lakes and ocean basins, deep-water mixing in the Baltic Sea is largely determined by mixing processes occurring in the energetic near-bottom region. Due to the complexity and small vertical extent of this region, however, previous modeling studies of the Baltic Sea have so far not been able to provide a numerically and physically sound representation of boundary mixing. Here we discuss first results from a nested high-resolution simulation of the central Baltic Sea that aims at a realistic description of the turbulent bottom boundary layer with the help of new numerical techniques (adaptive coordinates) and state-of-the-art turbulence modeling. Using a comprehensive data set from the Baltic Sea Tracer Release Experiment, we show that the model is able to reproduce the key dynamical processes (near-inertial waves, topographic waves, and a rim current) with excellent accuracy. Boundary mixing triggered by these processes was found to result in simulated basin-scale mixing rates in close agreement with observations, including a seasonal variability that has been emphasized in previous studies. These results may be relevant also for the description of mixing in large lakes and other stratified basins.

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“…The horizontal resolution of DKSS2013 is 3 NM in the main domain, 1 NM in the Wadden Sea, and 0.5 NM in the Transition Area. The model setup consists of vertical z-coordinates with a GETM at FCOO The Danish Defence Centre for Operational Oceanography (FCOO) runs three nested setups (Büchmann et al 2011) of the General Estuarine Transport Model, GETM, in operational production (Burchard et al 2009(Burchard et al , 2010 (Luyten et al 1999). …”

Section: Introductionmentioning

confidence: 99%

Uncertainty estimation for operational ocean forecast products—a multi-model ensemble for the North Sea and the Baltic Sea

Golbeck¹,

Li²,

Janssen³

et al. 2015

Ocean Dynamics

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Multi-model ensembles for sea surface temperature (SST), sea surface salinity (SSS), sea surface currents (SSC), and water transports have been developed for the North Sea and the Baltic Sea using outputs from several operational ocean forecasting models provided by different institutes. The individual models differ in model code, resolution, boundary conditions, atmospheric forcing, and data assimilation. The ensembles are produced on a daily basis. Daily statistics are calculated for each parameter giving information about the spread of the forecasts with standard deviation, ensemble mean and median, and coefficient of variation. High forecast uncertainty, i.e., for SSS and SSC, was found in the Skagerrak, Kattegat (Transition Area between North Sea and Baltic Sea), and the Norwegian Channel. Based on the data collected, longer-term statistical analyses have been done, such as a comparison with satellite data for SST and evaluation of the deviation between forecasts in temporal and spatial scale. Regions of high forecast uncertainty for SSS and SSC have been detected in the Transition Area and the Norwegian Channel where a large spread between the models might evolve due to differences in simulating the frontal structures and their movements. A distinct seasonal pattern could be distinguished for SST with high uncertainty between the forecasts during summer. Forecasts with relatively high deviation from the multi-model ensemble (MME) products or the other individual forecasts were detected for each region and each parameter. The comparison with satellite data showed that the error of the MME products is lowest compared to those of the ensemble members.

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Improvement of hydrodynamic forecasting of Danish waters: impact of low-frequency North Atlantic barotropic variations

Cited by 13 publications

References 7 publications

Internal variability of a 3-D ocean model

Internal variability of a 3-D ocean model

Deep‐water dynamics and boundary mixing in a nontidal stratified basin: A modeling study of the Baltic Sea

Uncertainty estimation for operational ocean forecast products—a multi-model ensemble for the North Sea and the Baltic Sea

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