Tsurane Kuragano

Global statistical space‐time scales of oceanic variability estimated from the TOPEX/POSEIDON altimeter data

Four years of the TOPEX/POSEIDON altimeter data are used to produce estimates of statistical space-time scales of ocean variabilities for the sea surface height. A three-dimensional (space-time) correlation function with an anisotropic directional dependence is assumed. The function has Gaussian distributions in radial directions in the space-time coordinates and ellipsoidal contour surfaces. The function can reveal the space-time scales and propagations of the variabilities and statistical errors in the altimeter data. We evaluated the space-time scales using the best fit correlation function to the altimeter data. The scales show geographical differences. This means that dominant variabilities depend on regions. We show an optimum interpolation (OI) method as an example of the application of the fitted correlation function. The OI is applied to the altimeter data in the space-time domain to make grid point data with higher accuracy than those obtained from a usual spatial (two-dimensional) OI (2-D OI). The values of the correlation function are used for the correlation coefficients of the first guess error in the OI. A propagation diagram obtained from the space-time OI (3-D OI) shows clear propagations of planetary disturbances with larger amplitude. The grid point values from the 3-D OI show better correspondences with sea levels from tide gauges than those from the 2-D OI. The advantage is due to the data of the different cycles being additionally adopted and the correlation function reflecting propagation of the ocean variabilities.

The 2004 Indian Ocean tsunami: Tsunami source model from satellite altimetry

Satellite altimetry measurements of sea surface heights for the first-time captured the Indian Ocean tsunami generated from the December 2004 great Sumatra earthquake. Analysis of the sea surface height profile suggests that the tsunami source, or the seafloor deformation, of the great earthquake propagated to the north at an extremely slow speed of less than 1 km/sec on average for the entire 1300-km-long segment along the northern Sumatra-Nicobar-Andaman Trench. The extremely slow propagation speed produces a very long duration of tens minutes, longer than earthquake source duration estimated (480–500 sec) from short-period P-wave radiation. The satellite altimetry data requires a total seismic moment of 9.86 × 1022 Nm (Mw=9.3). This estimate is approximately 2.5 times larger than the value from long-period surface wave analysis but nearly the same as that from the ultra-long-period normal mode study. The maximum amount of slip (∼30 m) is identified in an offshore region closest to the northern most part of Sumatra where the largest tsunami run-up heights were observed.

Short-Range Prediction Experiments with Operational Data Assimilation System for the Kuroshio South of Japan

The short-range (one month) variability of the Kuroshio path was predicted in 84 experiments (90-day predictions) using a model in an operational data assimilation system based on data from 1993 to 1999. The predictions started from an initial condition or members of a set of initial conditions, obtained in a reanalysis experiment. The predictions represent the transition from straight to meander of the Kuroshio path, and the results have been analyzed according to previously proposed mechanisms of the transition with eddy propagation and interaction acting as a trigger of the meander and self-sustained oscillation. The reanalysis shows that the meander evolves due to eddy activity. Simulation (no assimilation) shows no meander state, even with the same atmospheric forcing as the prediction. It is suggested therefore that the initial condition contains information on the meander and the system can represent the evolution. Mean (standard deviation) values of the axis error for all 84 cases are 13, 17, and 20 (10, 10, and 12) km, in 138.5°E, in the 30-, 60-, and 90-day predictions respectively. The observed mean deviation from seasonal variation is 30 km. The predictive limit of the system is thus about 80 days. The time scale of the limit depends on which stage in the transition is adopted as the initial condition. The gradual decrease of the amplitude in a stage from meander to straight paths is also predicted. The predictive limit is about 20 days, which is shorter than the prediction of the opposite transition.

Status and future of global and regional ocean prediction systems

Operational evolution of global and regional ocean forecasting systems has been extremely significant in recent years. Global Ocean Data Assimilation Experiment (GODAE) Oceanview supports the national research groups providing them with coordination and sharing expertise among the partners. Several systems have been set up and developed pre-operationally, and the majority of these are now fully operational; at the present time, they provide medium- and long-term forecasts of the most relevant ocean physical variables. These systems are based on ocean general circulation models and data-assimilation techniques that are able to correct the model with the information inferred from different types of observations. A few systems also incorporate a biogeochemical component coupled with the physical system, while others are based on coupled ocean–wave–ice–atmosphere models. The products are routinely validated with observations in order to assess their quality. Data and product implementation and organization, as well as service, for users have been well tried and tested, and most of the products are now available to users. The interaction with different users is an important factor in the development process. This paper provides a synthetic overview of the GODAE OceanView prediction systems.

Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project

AbstractOcean–sea ice reanalyses are crucial for assessing the variability and recent trends in the Arctic sea ice cover. This is especially true for sea ice volume, as long-term and large scale sea ice thickness observations are inexistent. Results from the Ocean ReAnalyses Intercomparison Project (ORA-IP) are presented, with a focus on Arctic sea ice fields reconstructed by state-of-the-art global ocean reanalyses. Differences between the various reanalyses are explored in terms of the effects of data assimilation, model physics and atmospheric forcing on properties of the sea ice cover, including concentration, thickness, velocity and snow. Amongst the 14 reanalyses studied here, 9 assimilate sea ice concentration, and none assimilate sea ice thickness data. The comparison reveals an overall agreement in the reconstructed concentration fields, mainly because of the constraints in surface temperature imposed by direct assimilation of ocean observations, prescribed or assimilated atmospheric forcing and assimilation of sea ice concentration. However, some spread still exists amongst the reanalyses, due to a variety of factors. In particular, a large spread in sea ice thickness is found within the ensemble of reanalyses, partially caused by the biases inherited from their sea ice model components. Biases are also affected by the assimilation of sea ice concentration and the treatment of sea ice thickness in the data assimilation process. An important outcome of this study is that the spatial distribution of ice volume varies widely between products, with no reanalysis standing out as clearly superior as compared to altimetry estimates. The ice thickness from systems without assimilation of sea ice concentration is not worse than that from systems constrained with sea ice observations. An evaluation of the sea ice velocity fields reveals that ice drifts too fast in most systems. As an ensemble, the ORA-IP reanalyses capture trends in Arctic sea ice area and extent relatively well. However, the ensemble can not be used to get a robust estimate of recent trends in the Arctic sea ice volume. Biases in the reanalyses certainly impact the simulated air–sea fluxes in the polar regions, and questions the suitability of current sea ice reanalyses to initialize seasonal forecasts.

Four-dimensional variational ocean reanalysis: a 30-year high-resolution dataset in the western North Pacific (FORA-WNP30)

We produced a four-dimensional variational ocean re-analysis for the Western North Pacific over 30 years (FORA-WNP30). It is the first-ever dataset covering the western North Pacific over 3 decades at eddy-resolving resolution. The four-dimensional variational analysis scheme version of the Meteorological Research Institute Multivariate Ocean Variational Estimation system (MOVE-4DVAR) is employed to conduct a long-term reanalysis experiment during 1982–2012. After evaluating the basic performance of FORA-WNP30, the interannual to decadal variability is analyzed. Overall, FORA-WNP30 reproduces basic features in the western North Pacific well. One of outstanding features in FORA-WNP30 is that anomalous events such as the Kuroshio large meander and anomalous intrusion of the Oyashio in the 1980s, when there were no altimeter data, are successfully reproduced. FORA-WNP30 is therefore a valuable dataset for a variety of oceanographic research topics and potentially for related fields such as climate study, meteorology and fisheries.

Evaluating the impacts of the tropical Pacific observing system on the ocean analysis fields in the global ocean data assimilation system for operational seasonal forecasts in JMA

Impacts of TAO/TRITON (TT) and Argo data in the tropical Pacific on the accuracy of temperature and salinity fields generated by a data-assimilation system for operational seasonal forecasts are examined through a series of observing system experiments. This study demonstrates positive impacts of both TT and Argo data, and that these observation types are complementary to each other. Data assimilation has relatively large impacts around the thermocline in the NINO3 region and the far western equatorial Pacific. A close relationship between the impacts of data assimilation and oceanic inherent uncertainty is suggested.

Balance of volume transports between horizontal circulation and meridional overturn in the North Pacific subarctic region

Horizontal and meridional volume transports on timescales from intra-seasonal to interannual in the North Pacific subarctic region were investigated using a reanalysis dataset for 1993–2001 that was constructed from an assimilation of the TOPEX altimeter and in situ data into an eddy-permitting North Pacific ocean general circulation model. The barotropic flow is excited along east of the Emperor Seamounts by the western intensification dynamics. The volume transport of this flow compensates for that across the interior region east of the Seamounts below the summit depth of the Seamounts. The Oyashio, which is also considered as a compensation flow for the transport in the whole interior region, includes baroclinic as well as barotropic components. Baroclinic transports in the whole interior region exceed those in the western boundary region in the upper (200–1000 m) and lower (2000–5000 m) layers, and the total transport is northward (southward) in the upper (lower) layer. These excesses of the baroclinic transport are balanced by a vertical transport of the meridional overturn. The meridional overturn has a complementary relation to the basin-scale baroclinic circulation in the North Pacific subactic region.

Data assimilation of sea ice concentration into a global ocean–sea ice model with corrections for atmospheric forcing and ocean temperature fields

A multivariate data assimilation experiment was conducted in order to improve the global representation of both the ocean and sea ice fields through the inclusion of sea ice concentration (SIC) data. Our method corrects the surface forcing and ocean temperature fields (as well as the SIC field) through the use of three-dimensional variational analysis. The adjustments to surface air temperatures resulting from the SIC assimilation are estimated on the basis of two constraints. First, we assume that the interfacial temperature difference between the surface air and the average value at the “top” of the grid (which represents a weighted mean according to the relative coverage of sea ice to open water within the grid) is maintained at the pre-assimilation value. Similarly, the vertical temperature structure for each of the five sea ice categories considered here remains unchanged throughout the assimilation. In making the necessary adjustments to upper-layer ocean temperatures, we again adopt a weighting procedure based on the condition that ice-free water temperature must remain the same. Thus, areas containing sea ice are allotted the freezing-point temperature such that the weighted mean value across the grid can be derived. The reproduction of the SIC field in both hemispheres is improved by incorporating the resulting corrections to the surface forcing and ocean temperature values, indicating that these boundary conditions produce results that are more consistent with the corrected SIC field in the sea ice model. The enhanced ocean–sea ice fields provide initial conditions that are better suited for coupled atmosphere–ocean–sea ice prediction experiments.

Altimeter's Capability of Reconstructing Realistic Eddy Fields Using Space-Time Optimum Interpolation

Sea surface height anomaly maps of realistic eddy activity were obtained by applying space-time optimum interpolation to altimeter data. Analysis error and rate of reconstructing eddy signals were investigated by taking account of: 1) dependency on orbit configurations of single and multiple altimeters; 2) dependency on space-time scales of realistic, dominant eddies; and 3) effect of space-time scales of eddy propagation. Large-scale sea surface height anomalies are subtracted from altimeter data by applying an along-track filter to allow easy handling of eddy signals. The space-time scales of the first-guess error in the optimum interpolation are statistically evaluated by fitting a space-time anisotropic Gaussian function to space-time-distributed correlation coefficients of sea surface height using the TOPEX data. The results of the optimum interpolation clarify the followings: 1) ERS has a better capability of reconstructing eddy signals than TOPEX. Comparison of maps from multi-altimeter data shows that TOPEX+ERS has a better capability than Jason−1+TOPEX in lower latitudes and vice versa in higher latitudes, though the differences are small. 2) The small space-time scale yields a low reconstruction rate in marginal seas and alongside the equator. The persistent timescale is large, and westward propagation is dominant in the subtropical and subarctic regions, where the reconstruction rates are high. 3) The optimum interpolation, taking account of eddy propagation, provides higher reconstruction rates than that taking no account of the propagation. The effect of propagation on the optimum interpolation is greater when it is applied to single-altimeter data than to multi-altimeter data.