Decadal-scale climate variations over the Pacific Ocean and its surroundings are strongly related to the so-called Pacific decadal oscillation (PDO) which is coherent with wintertime climate over North America and Asian monsoon, and have important impacts on marine ecosystems and fisheries. In a near-term climate prediction covering the period up to 2030, we require knowledge of the future state of internal variations in the climate system such as the PDO as well as the global warming signal. We perform sets of ensemble hindcast and forecast experiments using a coupled atmosphere-ocean climate model to examine the predictability of internal variations on decadal timescales, in addition to the response to external forcing due to changes in concentrations of greenhouse gases and aerosols, volcanic activity, and solar cycle variations. Our results highlight that an initialization of the upper-ocean state using historical observations is effective for successful hindcasts of the PDO and has a great impact on future predictions. Ensemble hindcasts for the 20th century demonstrate a predictive skill in the upper-ocean temperature over almost a decade, particularly around the Kuroshio-Oyashio extension (KOE) and subtropical oceanic frontal regions where the PDO signals are observed strongest. A negative tendency of the predicted PDO phase in the coming decade will enhance the rising trend in surface air-temperature (SAT) over east Asia and over the KOE region, and suppress it along the west coasts of North and South America and over the equatorial Pacific. This suppression will contribute to a slowing down of the global-mean SAT rise.climate change | data assimilation | decadal prediction | decadal variability | global warming A near-term climate prediction covering the period up to 2030 is a major issue to be addressed in the next assessment report of the Intergovernmental Panel on Climate Change (1, 2). To make the political decisions required to solve the socioeconomic problems arising from climate change over the coming decades, we need to take into account the large-scale climate changes associated with internal climate variability as well as the global warming signals (i.e., the response to external forcing due to changes in concentrations of greenhouse gases and aerosols, volcanic activity, and solar cycle variations) (3-6). A globally averaged surface-air-temperature (SAT) forecast up to 2030 depends little on specific socioeconomic scenarios or models used in centennial climate projection experiments (7, 8). On decadal timescales, SAT changes due to internal climate variability are comparable to those associated with global warming in magnitude (9). The predictability of internal climate variations is central to validating our skills in predicting the near-term climate variations.Prediction of internal decadal variability in the climate system represents one of the newest and toughest challenges. It is only recently that near-term climate projection experiments have been carried out focusing on internal decada...

[1] A four-dimensional variational (4D-VAR) data assimilation system using a coupled ocean-atmosphere global model has been successfully developed with the aim of better defining the dynamical states of the global climate on seasonal to interannual scales. The application of this system to state estimations of climate processes during the 1996-1998 period shows, in particular, that the representations of structures associated with several key events in the tropical Pacific and Indian Ocean sector (such as the El Niño, the Indian Ocean dipole, and the Asian summer monsoon) are significantly improved. This fact suggests that our 4D-VAR coupled data assimilation (CDA) approach has the potential to correct the initial location of the model climate attractor on the basis of observational data. In addition, the coupling parameters that control the air-sea exchange fluxes of mass, momentum, and heat become well adjusted. Such an initialization using the 4D-VAR CDA approach allows us to make a roughly 1.5-year lead time prediction of the 1997-1998 El Niño event. These results demonstrate that our 4D-VAR CDA system has the ability to enhance forecast potential for seasonal to interannual phenomena.

Recent observational surveys have shown significant oceanic bottom-water warming. However, the mechanisms causing such warming remain poorly understood, and their time scales are uncertain. Here, we report computer simulations that reveal a fast teleconnection between changes in the surface air-sea heat flux off the Adélie Coast of Antarctica and the bottom-water warming in the North Pacific. In contrast to conventional estimates of a multicentennial time scale, this link is established over only four decades through the action of internal waves. Changes in the heat content of the deep ocean are thus far more sensitive to the air-sea thermal interchanges than previously considered. Our findings require a reassessment of the role of the Southern Ocean in determining the impact of atmospheric warming on deep oceanic waters.

[1] We calculated basin-scale and global ocean decadal temperature change rates from the 1990s to the 2000s for waters below 3000 m. Large temperature increases were detected around Antarctica, and a relatively large temperature increase was detected along the northward path of Circumpolar Deep Water in the Pacific. The global heat content (HC) change estimated from the temperature change rates below 3000 m was 0.8 × 10 22 J decade; a value that cannot be neglected for precise estimation of the global heat balance. We reproduced the observed temperature changes in the deep ocean using a data assimilation system and examined virtual observations in the reproduced data field to evaluate the uncertainty of the HC changes estimated from the actual temporally and spatially sparse observations. From the analysis of the virtual observations, it is shown that the global HC increase below 3000 m during recent decades can be detected using the available observation system of periodic revisits to the same sampling sections, although the uncertainty is large.

A 4‐dimensional variational data assimilation system has been used to better define the mean seasonal state of the North Pacific. The synthesis of available observational records and a sophisticated general circulation model produces a dynamically consistent time‐varying dataset which exhibits realistic features of the global ocean circulation and requires no artificial sources or sinks for the temperature and salinity fields. The dataset enables us to clarify the water mass formation and movement processes. A sensitivity experiment using our system reveals that the origin of the North Pacific Intermediate Water can be traced back to the Okhotsk and Bering Seas in the subarctic region and to the subtropical Kuroshio region further south, consistent with recent observational findings. This result illustrates that the ocean state derived from our data assimilation has greater information and forecast potential than that obtained from earlier methods.

[1] A newly developed ocean reanalysis dataset (1987 -2004) has been used to study interannual variability in the temperature inversion that characterizes the upper layer of the subarctic North Pacific. A local minimum in temperature at the upper edge of the inversion exhibits a significant interannual variability. Conversely, fluctuations in the local temperature maximum (T max ) found at the lower edge are relatively small. In the T max case, the variation in the depth of T max is significant and is closely related to ENSO variability. Our estimate of the adjoint solution shows that during periods when the T max is located at shallower (deeper) depths, the source water comes from both the Gulf of Alaska and the Kuroshio Extension region (only from the Kuroshio Extension region). These facts suggest that the depth of T max is an important indication for diagnostics of the interannual variability of water-mass property in the subarctic Pacific.

A long‐term data synthesis experiment was conducted for the period 1957–2011 using a modified quasi‐global four‐dimensional variational data assimilation system that was originally developed to improve the representation of the deep ocean, including a unique method for anomaly assimilation. The overall characteristics of the resulting ocean state estimate, which is dynamically consistent without any artificial sources or sinks for heat and salt, are evaluated in the Pacific Ocean. It is shown that the data set better represents the comprehensive ocean state: the mean state of the water mass distribution and volume transport and components of temporal variability from the sea surface to the bottom on interannual to multidecadal timescales. This suggests that the data set can be used to examine interactions between temporal variations throughout the entire depth range and is useful for understanding ocean physics and its role in the climate system.

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