Tamaki Yasuda

Long-Term Variability of North Pacific Subtropical Mode Water in Response to Spin-Up of the Subtropical Gyre

The Meteorological Research Institutes ocean general circulation model (MRI-OGCM) has been used to investigate the temperature variability of the North Pacific Subtropical Mode Water (NPSTMW) over a time series longer than 5 years via the spin-up of the subtropical gyre. Besides an interannual variation, the wintertime sea surface temperature in the area where the NPSTMW is formed, and the temperature of the NPSTMW itself, both change remarkably in a >5-year time scale. An analysis of heat budgets showed that the long-term changes in NPSTMW temperature are due mainly to a leading advection of heat by the Kuroshio Extension and compensating surface heat flux. As a result of a dynamical adjustment to the wind stress fields, the transports of the Kuroshio and the Kuroshio Extension increased in the mid 1970s with a lag of 3 years after the wind stress curl in the central North Pacific. The increased heat advection by the Kuroshio Extension induces a warming in the mixed layer in the NPSTMW formation area, followed by a warming of the NPSTMW itself. Both these warming actions increase the heat release to the atmosphere. These results imply that the surface heat flux over the Kuroshio Extension area varies in response to the change in the ocean circulation through the spin-up of the subtropical gyre.

Generation mechanism of quasidecadal variability of upper ocean heat content in the equatorial Pacific Ocean

[1] We investigate the relationship between oceanic and atmospheric anomaly fields in the tropical Pacific on the quasidecadal (QD; 10 to 18 years) scale is explored. It is suggested that the anomalous wind stress curl in the tropical South Pacific can generate the equatorial upper ocean heat content (OHC) anomalies on the QD scale via the Sverdrup transport resembling that of the recharge-discharge oscillator model for the dynamics of El Nino-Southern Oscillation (ENSO). The results also show that the wind stress curl anomalies in the tropical South Pacific are accompanied by ENSO-like, QD-scale anomaly patterns of the sea level pressure and sea surface temperature. Furthermore, the results indicate that the equatorial QD-scale OHC anomalies can also be generated by the ENSO-like propagations of the OHC anomalies in the tropical North and South Pacific. This study presents a systematic linkage between the ENSO-like, QD-scale OHC propagation in the tropical North and South Pacific, the Sverdrup transport variation in the tropical South Pacific, and the ENSO-like, QD-scale SST and SLP anomalies in the tropical Pacific.

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.

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2): atmosphere–land–ocean–sea ice coupled prediction system for operational seasonal forecasting

This paper describes the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2), which was put into operation in June 2015 for the purpose of performing seasonal predictions. JMA/MRI-CPS2 has various upgrades from its predecessor, JMA/MRI-CPS1, including improved resolution and physics in its atmospheric and oceanic components, introduction of an interactive sea-ice model and realistic initialization of its land component. Verification of extensive re-forecasts covering a 30-year period (1981–2010) demonstrates that JMA/MRI-CPS2 possesses improved seasonal predictive skills for both atmospheric and oceanic interannual variability as well as key coupled variability such as the El Niño–Southern Oscillation (ENSO). For ENSO prediction, the new system better represents the forecast uncertainty and transition/duration of ENSO phases. Our analysis suggests that the enhanced predictive skills are attributable to incremental improvements resulting from all of the changes, as is apparent in the beneficial effects of sea-ice coupling and land initialization on 2-m temperature predictions. JMA/MRI-CPS2 is capable of reasonably representing the seasonal cycle and secular trends of sea ice. The sea-ice coupling remarkably enhances the predictive capability for the Arctic 2-m temperature, indicating the importance of this factor, particularly for seasonal predictions in the Arctic region.