Coupled ocean–atmosphere modeling and predictions

Miller, Arthur J.; Collins, Matthew; Gualdi, Silvio; Jensen, Tommy G.; Misra, Vasubandhu; Pezzi, Luciano Ponzi; Pierce, David W.; Putrasahan, Dian; Seo, Hyodae; Tseng, Yu‐Heng

doi:10.1357/002224017821836770

Cited by 16 publications

(6 citation statements)

References 183 publications

(226 reference statements)

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“…The model has been widely used for many applications such as ocean circulation forecast [ Haidvogel et al . ], biophysical modeling [ Dias et al ., ; D'agostini et al ., ], biogeochemical modeling [ Arruda et al ., ] and for air‐sea interaction studies [ Putrasahan et al ., ; Seo et al ., ; Miller et al ., ; Pullen et al ., ].…”

Section: Methodsmentioning

confidence: 99%

Regional modeling of the water masses and circulation annual variability at the Southern Brazilian Continental Shelf

Mendonça

Souza

Aseff

et al. 2017

J. Geophys. Res. Oceans

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The Southern Brazilian Continental Shelf (SBCS) is one of the more productive areas for fisheries in Brazilian waters. The water masses and the dynamical processes of the region present a very seasonal behavior that imprint strong effects in the ecosystem and the weather of the area and its vicinity. This paper makes use of the Regional Ocean Modeling System (ROMS) for studying the water mass distribution and circulation variability in the SBCS during the year of 2012. Model outputs were compared to in situ, historical observations and to satellite data. The model was able to reproduce the main thermohaline characteristics of the waters dominating the SBCS and the adjacent region. The mixing between the Subantarctic Shelf Water and the Subtropical Shelf Water, known as the Subtropical Shelf Front (STSF), presented a clear seasonal change in volume. As a consequence of the mixing and of the seasonal oscillation of the STSF position, the stability of the water column inside the SBCS also changes seasonally. Current velocities and associated transports estimated for the Brazil Current (BC) and for the Brazilian Coastal Current (BCC) agree with previous measurements and estimates, stressing the fact that the opposite flow of the BCC occurring during winter in the study region is about 2 orders of magnitude smaller than that of the BC. Seasonal maps of simulated Mean Kinetic Energy and Eddy Kinetic Energy demonstrate the known behavior of the BC and stressed the importance of the mean coastal flow off Argentina throughout the year.

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Section: Methodsmentioning

confidence: 99%

Regional modeling of the water masses and circulation annual variability at the Southern Brazilian Continental Shelf

Mendonça

Souza

Aseff

et al. 2017

J. Geophys. Res. Oceans

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“…These drivers equally apply on longer timescales, over which the impact of feedbacks on the mean state and extremes may be more significant, and a full Earth system approach may prove to be beneficial for developing relevant regional-scale information of use for planning and policy-making applications (e.g. Miller et al, 2017). 5…”

Section: Motivations For Regional Coupled Model Developmentmentioning

confidence: 99%

The UKC3 regional coupled environmental prediction system

Lewis¹,

Sanchez²,

Fallmann³

et al. 2018

Preprint

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Abstract. This paper describes an updated configuration of the regional coupled research system, termed UKC3, developed and evaluated under the UK Environmental Prediction collaboration. This represents a further step towards a vision of simulating the numerous interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land using more integrated regional coupled prediction systems at km-scale resolution. The UKC3 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean surface waves (WAVEWATCH III), coupled together using OASIS3-MCT libraries. The major update introduced since the UKC2 configuration is an explicit representation of wave processes in the ocean and their feedbacks through wave-to-ocean coupling. Ocean model results demonstrate that wave coupling, in particular representing the wave modified surface drag, has a small but positive improvement on the agreement between simulated sea surface temperatures and in situ observations, relative to simulations without wave feedbacks. Other incremental developments to the coupled modelling capability introduced since the UKC2 configuration are also detailed. Coupled regional prediction systems are of interest for applications across a range of timescales, from hours to decades ahead. The first results of simulations run over extended periods, covering four experiments each of order one month in duration are therefore analysed and discussed in the context of further characterising the potential benefits of coupled prediction on forecast skill, and on the stability of such systems over longer time periods. Results across atmosphere, ocean and wave components are shown to be of at least comparable skill to the equivalent uncoupled control simulations, with notable improvements demonstrated in surface temperature and wave state predictions in some near-coastal regions, and in wind speeds over the sea.

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“…Earth system models simulate different components like the ocean, atmosphere, sea ice and land surface and their interactions in one framework including fluxes in between them which can thus lead to more consistent simulations than standalone models. Such models can capture the key physical processes for each of the coupled components and can help to better understand the impact of changes in one component on other components (Miller et al ., 2017). However, when using a coupled Earth system model, it can only represent a climatological state, but not the current weather conditions unless one uses data assimilation (DA) to initialize the model forecasts.…”

Section: Introductionmentioning

confidence: 99%

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

Tang

Sidorenko

et al. 2020

Quart J Royal Meteoro Soc

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An ensemble-based data assimilation framework for a coupled oceanatmosphere model is applied to investigate the influence of assimilating different types of ocean observations on the ocean and atmosphere simulation. The data assimilation is performed with the parallel data assimilation framework (PDAF) for the climate model AWI-CM. Observations of the ocean, namely satellite sea-surface temperature (SST) and temperature and salinity profiles, are assimilated into the ocean component. The atmospheric state is only influenced by the model dynamics. Different assimilation scenarios were carried out with different combinations of observations to investigate to what extent the assimilation into the coupled model leads to a better estimation of the state of the ocean as well as the atmosphere. The influence of the data assimilation is assessed by comparing the ocean prediction with dependent and independent ocean observations. For the atmosphere, the assimilation result is compared with the ERA-Interim atmospheric reanalysis data. The ocean temperature and salinity are improved by all the assimilation scenarios in the coupled system. The assimilation leads to a response of the atmosphere throughout the troposphere and impacts the global atmospheric circulation. Globally the temperature and wind speed are improved in the atmosphere on average. K E Y W O R D S coupled model, data assimilation, sea-surface temperature, temperature and salinity profiles 1 INTRODUCTION Traditionally, different components of the Earth system such as the ocean and the atmosphere are simulated by separate models with influences of other components being modelled as boundary conditions or forcings. However, the oceans and the atmosphere are connected and interact with each other. A consistent initial condition for these different components is required and is expected to provide a better forecast for both the ocean and the atmosphere. Earth system models simulate different components like the ocean, atmosphere, sea ice and land This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Coupled ocean–atmosphere modeling and predictions

Cited by 16 publications

References 183 publications

Regional modeling of the water masses and circulation annual variability at the Southern Brazilian Continental Shelf

Regional modeling of the water masses and circulation annual variability at the Southern Brazilian Continental Shelf

The UKC3 regional coupled environmental prediction system

Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data

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