Antarctic circumpolar modes in a coupled ocean–atmosphere model

Eastward-propagating patterns in anomalous potential temperature and salinity of the Southern Ocean are analyzed in the output of a 1000-year simulation of the global coupled atmosphere–ocean GCM ECHO-G. Such features can be associated with the so-called Antarctic Circumpolar Wave (ACW). It is found that time–longitude diagrams that have traditionally been used to aid the visualization of the ACW are strongly influenced by the width of the bandpass time filtering. This is due to the masking of considerable low-frequency variability that occurs over a broad range of time scales. Frequency–wavenumber analysis of the ACW shows that the eastward-propagating waves do have preferred spectral peaks, but that both the period and wavenumber change erratically when comparing different centuries throughout the simulation. The variability of the ACW on a variety of time scales from interannual to centennial suggests that the waiting time for a sufficient observational record to determine the time scale of variability of the real world ACW (and the associated decadal time scale predictability of climate for southern landmasses) will be a very long one.Responsible Editor: Dirk Olbers     

Regional coupled ocean–atmosphere downscaling in the Southeast Pacific: impacts on upwelling, mesoscale air–sea fluxes, and ocean eddies

Ocean–atmosphere coupling in the Humboldt Current System (HCS) of the Southeast Pacific is studied using the Scripps Coupled Ocean–atmosphere Regional (SCOAR) model, which is used to downscale the National Center for Environmental Prediction (NCEP) Reanalysis-2 (RA2) product for the period 2000–2007 at 20-km resolution. An interactive 2-D spatial smoother within the sea-surface temperature (SST)–flux coupler is invoked in a separate run to isolate the impact of the mesoscale (～50–200 km, in the oceanic sense) SST field felt by the atmosphere in the fully coupled run. For the HCS, SCOAR produces seasonal wind stress and wind stress curl patterns that agree better with QuikSCAT winds than those from RA2. The SCOAR downscaled wind stress distribution has substantially different impacts on the magnitude and structure of wind-driven upwelling processes along the coast compared to RA2. Along coastal locations such as Arica and Taltal, SCOAR and RA2 produce seasonally opposite signs in the total wind-driven upwelling transport. At San Juan, SCOAR shows that upwelling is mainly due to coastal Ekman upwelling transport, while in RA2 upwelling is mostly attributed to Ekman pumping. Fully coupled SCOAR shows significant SST–wind stress coupling during fall and winter, while smoothed SCOAR shows insignificant coupling throughout, indicating the important role of ocean mesoscale eddies on air–sea coupling in HCS. Coupling between SST, wind speed, and latent heat flux is incoherent in large-scale coupling and full coupling mode. In contrast, coupling between these three variables is clearly identified for oceanic mesoscales, which suggests that mesoscale SST affects latent heat directly through the bulk formulation, as well as indirectly through stability changes on the overlying atmosphere, which affects surface wind speeds. The SST–wind stress and SST–heat-flux couplings, however, fail to produce a strong change in the ocean eddy statistics. No rectified effects of ocean–atmosphere coupling were identified for either the atmospheric or oceanic mean conditions, suggesting that mesoscale coupling is too weak in this region to strongly alter the basic climate state.     

A three-dimensional surface wave–ocean circulation coupled model and its initial testing

A theoretical framework to include the influences of nonbreaking surface waves in ocean general circulation models is established based on Reynolds stresses and fluxes terms derived from surface wave-induced fluctuation. An expression for the wave-induced viscosity and diffusivity as a function of the wave number spectrum is derived for infinite and finite water depths; this derivation allows the coupling of ocean circulation models with a wave number spectrum numerical model. In the case of monochromatic surface wave, the wave-induced viscosity and diffusivity are functions of the Stokes drift. The influence of the wave-induced mixing scheme on global ocean circulation models was tested with the Princeton Ocean Model, indicating significant improvement in upper ocean thermal structure and mixed layer depth compared with mixing obtained by the Mellor–Yamada scheme without the wave influence. For example, the model–observation correlation coefficient of the upper 100-m temperature along 35° N increases from 0.68 without wave influence to 0.93 with wave influence. The wave-induced Reynolds stress can reach up to about 5% of the wind stress in high latitudes, and drive 2–3 Sv transport in the global ocean in the form of mesoscale eddies with diameter of 500–1,000 km. The surface wave-induced mixing is more pronounced in middle and high latitudes during the summer in the Northern Hemisphere and in middle latitudes in the Southern Hemisphere.     

Investigation of hurricane Ivan using the coupled ocean–atmosphere–wave–sediment transport (COAWST) model

The coupled ocean–atmosphere–wave–sediment transport (COAWST) model is used to hindcast Hurricane Ivan (2004), an extremely intense tropical cyclone (TC) translating through the Gulf of Mexico. Sensitivity experiments with increasing complexity in ocean–atmosphere–wave coupled exchange processes are performed to assess the impacts of coupling on the predictions of the atmosphere, ocean, and wave environments during the occurrence of a TC. Modest improvement in track but significant improvement in intensity are found when using the fully atmosphere–ocean-wave coupled configuration versus uncoupled (e.g., standalone atmosphere, ocean, or wave) model simulations. Surface wave fields generated in the fully coupled configuration also demonstrates good agreement with in situ buoy measurements. Coupled and uncoupled model-simulated sea surface temperature (SST) fields are compared with both in situ and remote observations. Detailed heat budget analysis reveals that the mixed layer temperature cooling in the deep ocean (on the shelf) is caused primarily by advection (equally by advection and diffusion).     

Numerical experiments of air–ice drag coefficient and its impact on ice–ocean coupled system in the Sea of Okhotsk

Sensitivity study of the air–ice drag coefficient C _Dai is presented with an ice–ocean coupled model for the Sea of Okhotsk. The C _Dai?×?10³ value is varied from 2 to 5 based on the direct measurements in the region. The maximum volume transport of the East Sakhalin Current and the mean sea ice velocity were intensified as C _Dai increased. The sensitivity experiment with the ice–water drag coefficient C _Diw showed that the East Sakhalin Current volume transport is hardly affected by C _Diw but significantly intensified by C _Dai. While the ice drift in the off-ice-edge direction was intensified by the increase in C _Dai and the decrease in C _Diw, the ice edge location was nearly unchanged. This was due to melting caused by the relatively warm water inflow from the North Pacific. That is, sea ice extent in the region is strongly influenced by melting caused by a large ice–ocean heat transfer. In the active melting regions, the ice–ocean heat transfer of more than 100 W/m² occurred even in mid-winter. This is the same order as the cooling by air in winter, and a heat insulation capacity of sea ice is weakened in such regions.     

Sunlight and tidal interaction as a mechanism of carbon transport in an ice-covered region: Results of a coupled ice–ocean ecosystem model

In order to clarify the mechanism of carbon transport in an ice-covered ecosystem in Lake Saroma (44^°N${44}^{°}\mathrm{N}$, 143^°E${143}^{°}\mathrm{E}$, Hokkaido, Japan), a three-dimensional numerical calculation using a coupled ice–ocean ecosystem model was conducted. This model comprises an ocean ecosystem model, an ice ecosystem model, and equations for the coupling between ice and ocean. Comparisons of calculated results with observational data confirm that the calculation well reproduced the in situ phenomena with respect to tides, tidal currents, concentrations of POC and chlorophyll a in ice and in water, and sinking fluxes beneath the ice. The analysis of the organic carbon budget based on the calculation reveals that tide-induced transport, the enhancement of biological production in a pelagic system, and the physical release of organic matter from ice associated with ice-melting are important factors affecting the carbon transport during the ice-melting season. The carbon transport has a one-day time cycle. This is because principal driving forces are sunlight, and diurnal tides. The described mechanism of “sunlight and tidal pumping” is one of the most important features of carbon transport in a coupled ice–water ecosystem.     

Assimilation of ice concentration in a coupled ice–ocean model,using the Ensemble Kalman filter

An implementation of the Ensemble Kalman filter (EnKF) with a coupled ice–ocean model is presented. The model system consists of a dynamic–thermodynamic ice model using the elastic-viscous-plastic (EVP) rheology coupled with the HYbrid Coordinate Ocean Model (HYCOM). The observed variable is ice concentration from passive microwave sensor data (SSM/I). The assimilation of ice concentration has the desired effect of reducing the difference between observations and model. Comparison of the assimilation experiment with a free-run experiment shows that there are large differences, especially in summer. In winter the differences are relatively small, partly because the atmospheric forcing used to run the model depends upon SSM/I data. The assimilation has the strongest impact close to the ice edge, where it ensures a correct location of the ice edge throughout the simulation. An inspection of the model ensemble statistics reveals that the error estimates of the model are too small in winter, partly a result of too low model ice-concentration variance in the central ice pack. It is found that the ensemble covariance between ice concentration and sea-surface temperature in the same grid cell is of the same sign (negative) throughout the year. The ensemble covariance between ice concentration and salinity is more dependent upon the physical mechanisms involved, with ice transport and freeze/melt giving different signs of the covariances. The ice-transport and ice-melt mechanisms also impact the ice-concentration variance and the covariance between ice concentration and ice thickness. The ensemble statistics show a high degree of complexity, which to some extent merits the use of computationally expensive assimilation methods, such as the Ensemble Kalman filter. The present study focuses on the assimilation of ice concentration, but it is understood that assimilation of other datasets, such as sea-surface temperature, would be beneficial.Responsible Editor: Jin-Song von Storch     

Turbulent heat fluxes during an intense cold-air outbreak over the Kuroshio Extension Region: results from a high-resolution coupled atmosphere–ocean model

The coupled ocean atmosphere mesoscale prediction system that includes the Navy Coastal Ocean Model has been configured for the Kuroshio Extension region using multiple one-way nested high-resolution grids. The coupled model system was used to simulate a strong cold-air outbreak event from 31 Jan to 7 Feb 2005 in good agreement with meteorological data from a surface buoy data and QuikSCAT scatterometer winds. Latent heat fluxes and sensible heat fluxes were computed during the event with daily averages in excess of 1,500 W/m² and 500 W/m², respectively, and combined instantaneous turbulent heat fluxes up to 2,300 W/m². The largest heat fluxes were found in two large meanders of the Kuroshio and along its southern flank. Strong gradients in turbulent heat fluxes coincided with strong sea surface temperature gradients and were maintained during the cold-air outbreak simulation. The large turbulent heat fluxes lead to significant subtropical mode water formation during the event at a rate about 10 Sv in the cyclonic recirculation region south of the Kuroshio. This increased the volume of core layer mode water within the temperature range 16°C to 18°C by 10% and increased the surface area of that layer directly exposed to the atmosphere by a factor close to 5 in the model domain.     

Probabilistic seismic performance and loss assessment of a bridge–foundation–soil system

This paper presents the probabilistic seismic performance and loss assessment of an actual bridge–foundation–soil system, the Fitzgerald Avenue twin bridges in Christchurch, New Zealand. A two-dimensional finite element model of the longitudinal direction of the system is modelled using advanced soil and structural constitutive models. Ground motions at multiple levels of intensity are selected based on the seismic hazard deaggregation at the site. Based on rigorous examination of several deterministic analyses, engineering demand parameters (EDP's), which capture the global and local demand, and consequent damage to the bridge and foundation are determined. A probabilistic seismic loss assessment of the structure considering both direct repair and loss of functionality consequences was performed to holistically assess the seismic risk of the system.It was found that the non-horizontal stratification of the soils, liquefaction, and soil–structure interaction had pronounced effects on the seismic demand distribution of the bridge components, of which the north abutment piles and central pier were critical in the systems seismic performance. The consequences due to loss of functionality of the bridge during repair were significantly larger than the direct repair costs, with over a 2% in 50 year probability of the total loss exceeding twice the book-value of the structure.     

On the “spring predictability barrier” for strong El Nio events as derived from an intermediate coupled model ensemble prediction system

Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the "spring predictability barrier"(SPB) problem for the 2015/16 strong El Nio event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nio event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nio occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nio predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nio event. By investigating the model errors represented by the tendency errors for the SSTA component,we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nio event and explain why the 2015/16 El Nio was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector(NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nio events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nio events could be greatly enhanced by using the NFSV-like tendency error to correct the model.     

Numerical prediction of ground vibrations induced by high-speed trains including wheel–rail–soil coupled effects

A simplified analytical model including the coupled effects of the wheel–rail–soil system and geometric irregularities of the track is proposed for evaluation of the moving train load. The wheel–rail–soil system is simulated as a series of moving point loads on an Euler–Bernoulli beam resting on a visco-elastic half-space, and the wave-number transform is adopted to derive the 2.5D finite element formulation. The numerical model is validated by published data in the literature. Numerical predictions of ground vibrations by using the proposed method are conducted at a site on the Qin-Shen Line in China.     

Modelling ocean wave climate with a Bayesian hierarchical space–time model and a log-transform of the data

Long-term trends in the ocean wave climate because of global warming are of major concern to many stakeholders within the maritime industries, and there is a need to take severe sea state conditions into account in design of marine structures and in marine operations. Various stochastic models of significant wave height are reported in the literature, but most are based on point measurements without exploiting the flexible framework of Bayesian hierarchical space–time models. This framework allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge, yet remains intuitive and easily interpreted. This paper presents a Bayesian hierarchical space–time model with a log-transform for significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined, and the results from applying the model to data of different temporal resolutions will be discussed. Different model alternatives have been tried and long-term trends in the data have been identified for all model alternatives. Overall, these trends are in reasonable agreement and also agree fairly well with previous studies. The log-transform was included in order to account for observed heteroscedasticity in the data, and results are compared to previous results where a similar model was employed without a log-transform. Furthermore, a discussion of possible extensions to the model, e.g. incorporating regression terms with relevant meteorological data, will be presented.     

Impact of different sea surface roughness on surface gravity waves using a coupled atmosphere–wave model: a case of Hurricane Isaac (2012)

Ocean Dynamics - The complicated pattern of the chaotic ocean surface depends strongly on the interaction between wind and waves. An accurate representation of momentum and energy exchange at...     

Seagrass meadows globally as a coupled social–ecological system: Implications for human wellbeing

Seagrass ecosystems are diminishing worldwide and repeated studies confirm a lack of appreciation for the value of these systems. In order to highlight their value we provide the first discussion of seagrass meadows as a coupled social–ecological system on a global scale. We consider the impact of a declining resource on people, including those for whom seagrass meadows are utilised for income generation and a source of food security through fisheries support. Case studies from across the globe are used to demonstrate the intricate relationship between seagrass meadows and people that highlight the multi-functional role of seagrasses in human wellbeing. While each case underscores unique issues, these examples simultaneously reveal social–ecological coupling that transcends cultural and geographical boundaries. We conclude that understanding seagrass meadows as a coupled social–ecological system is crucial in carving pathways for social and ecological resilience in light of current patterns of local to global environmental change.     

A three-dimensional variational ocean data assimilation system:Scheme and preliminary results

A new 3DVAR-based Ocean Variational Analysis System (OVALS) is developed. OVALS is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data. As a component of OVALS, a new variational scheme is proposed to assimilate the sea surface height data. This scheme considers both the vertical correlation of background errors and the nonlinear temperature-salinity relationship which is derived from the generalization of the linear balance constraints to the nonlinear in the 3DVAR. By this scheme, the model temperature and salinity fields are directly adjusted from the altimetry data. Additionally, OVALS can assimilate the temperature and salinity profiles from the ARGO floats which have been implemented in recent years and some temperature and salinity data such as from expendable bathythermograph, moored ocean buoys, etc. A 21-year assimilation experiment is carried out by using OVALS and the Tropical Pacific circulation model. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations. Moreover, the root mean square errors of temperature and salinity in the upper depth less than 420 m reach 0.63℃ and 0.34 psu.