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This paper evaluates in a realistic context the local contributions of direct atmospheric forcing and intrinsic oceanic processes on interannual sea level anomalies (SLAs). A ¼° global ocean-sea ice general circulation model, driven over 47 yr by the full range of atmospheric time scales, is quantitatively assessed against altimetry and shown to reproduce most observed features of the interannual SLA variability from 1993 to 2004. Comparing this simulation with a second driven only by the climatological annual cycle reveals that the intrinsic part of the total interannual SLA variance exceeds 40%% over half of the open-ocean area and exceeds 80%% over one-fifth of it. This intrinsic contribution is particularly strong in eddy-active regions (more than 70%%-80%% in the Southern Ocean and western boundary current extensions) as predicted by idealized studies, as well as within the 20°-35° latitude bands. The atmosphere directly forces most of the interannual SLA variance at low latitudes and in most midlatitude eastern basins, in particular north of about 40°N in the Pacific. The interannual SLA variance is almost entirely due to intrinsic processes south of the Antarctic Circumpolar Current in the Indian Ocean sector, while half of this variance is forced by the atmosphere north of it. The same simulations were performed and analyzed at 2° resolution as well: switching to this laminar regime yields a comparable forced variability (large-scale distribution and magnitude) but almost suppresses the intrinsic variability. This likely explains why laminar ocean models largely underestimate the interannual SLA variance. [ABSTRACT FROM AUTHOR]

Improved seasonal prediction of sea surface temperature (SST) anomalies over the global oceans is the theme of this paper. Using 13 state-of-the-art coupled global atmosphere–ocean models and 13 yr of seasonal forecasts, the performance of individual models, the ensemble mean, the bias-removed ensemble mean, and the Florida State University (FSU) superensemble are compared. A total of 23 400 seasonal forecasts based on 1-month lead times were available for this study. Evaluation metrics include both deterministic and probabilistic skill measures, such as verification of anomalies based on model and observed climatology, time series of specific climate indices, standard deterministic ensemble mean scores including anomaly correlations, root-mean-square (RMS) errors, and probabilistic skill measures such as equitable threat scores for seasonal SST forecasts. This study also illustrates the Niño-3.4 SST forecast skill for the equatorial Pacific Ocean and for the dipole index for the Indian Ocean. The relative skills of total SST fields and of the SST anomalies from the 13 coupled atmosphere–ocean models are presented. Comparisons of superensemble-based seasonal forecasts with recent studies on SST anomaly forecasts are also shown. Overall it is found that the multimodel superensemble forecasts are characterized by considerable RMS error reductions and increased accuracy in the spatial distribution of SST. Superensemble SST skill also persists for El Niño and La Niña forecasts since the large comparative skill of the superensemble is retained across such years. Real-time forecasts of seasonal sea surface temperature anomalies appear to be possible. [ABSTRACT FROM AUTHOR]

Measurements of the turbulent kinetic energy dissipation rate (ε) were conducted by a free-fall microstructure profiler in the western Pacific North Equatorial Current (WPNEC) during a continuous period of 25 h, from the sea surface to about 160 m depth. In the mixed layer (ML), ε values were typically on the order of 10−8∼10−7 W kg−1, and an obvious diurnal cycle existed in the upper 40 m of the surface mixing layer. Below the ML, ε was reduced to 10−9∼10−8 W kg−1 with some patches of high ε reaching 10−7.5 W kg−1. The barrier layer was identified in the nighttime with a maximum thickness of 20 m, and it was eroded by the advection of freshwater within the lower part of the isothermal layers associated with an anticyclonic eddy in the afternoon. A simple scaling relevant to shear (S2) instability and stratification (N2) that can predict turbulent dissipation rates in the transition layer, between the well-mixed layer and the thermocline below, was obtained through the scaling ε ∼ S − 0.40 N 0.20 . Besides turbulence, double-diffusive processes also contributed to the vertical mixing levels in the upper WPNEC. [ABSTRACT FROM AUTHOR]

Although great progress has been made to advance the scientific understanding of oil spills, tools for integrated assessment modeling of the long-term impacts on ecosystems, socioeconomics and human health are lacking. The objective of this study was to develop a conceptual framework that could be used to answer stakeholder questions about oil spill impacts and to identify knowledge gaps and future integration priorities. The framework was initially separated into four knowledge domains (ocean environment, biological ecosystems, socioeconomics, and human health) whose interactions were explored by gathering stakeholder questions through public engagement, assimilating expert input about existing models, and consolidating information through a system dynamics approach. This synthesis resulted in a causal loop diagram from which the interconnectivity of the system could be visualized. Results of this analysis indicate that the system naturally separates into two tiers, ocean environment and biological ecosystems versus socioeconomics and human health. As a result, ocean environment and ecosystem models could be used to provide input to explore human health and socioeconomic variables in hypothetical scenarios. At decadal-plus time scales, the analysis emphasized that human domains influence the natural domains through changes in oil-spill related laws and regulations. Although data gaps were identified in all four model domains, the socioeconomics and human health domains are the least established. Considerable future work is needed to address research gaps and to create fully coupled quantitative integrative assessment models that can be used in strategic decision-making that will optimize recoveries from future large oil spills.