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The NCAR Community Climate System Model, version 3 (CCSM3) exhibits persistent errors in its simulation of the El Niño-Southern Oscillation (ENSO) mode of coupled variability. The amplitude of the oscillation is too strong, the dominant 2-yr period too regular, and the width of the sea surface temperature response in the Pacific too narrow, with positive anomalies extending too far into the western Pacific. Two changes in the parameterization of deep convection result in a significant improvement to many aspects of the ENSO simulation. The inclusion of convective momentum transport (CMT) and a dilution approximation for the calculation of convective available potential energy (CAPE) are used in development integrations, and a striking improvement in ENSO characteristics is seen. An increase in the periodicity of ENSO is achieved by a reduction in the strength of the existing "short-circuited" delayed-oscillator mode. The off-equatorial response is weaker and less tropically confined, largely as a result of the CMT and an associated redistribution of zonal momentum. The Pacific east-west structure is improved in response to the presence of convective dilution and cooling provided by increased surface fluxes. The initiation of El Niño events is fundamentally different. Enhanced intraseasonal surface stress variability leads to absolute surface westerlies and a cooling-warming dipole between the Philippine Sea and western Pacific. Lag-regression analysis shows that intraseasonal variability may play a significant role in event initiation and maintenance as opposed to being a benign response to increased SSTs. Recent observational evidence appears to support such a leading relationship.

The ocean component of the Community Climate System Model version 4 (CCSM4) is described, and its solutions from the twentieth-century (20C) simulations are documented in comparison with observations and those of CCSM3. The improvements to the ocean model physical processes include new parameterizations to represent previously missing physics and modifications of existing parameterizations to incorporate recent new developments. In comparison with CCSM3, the new solutions show some significant improvements that can be attributed to these model changes. These include a better equatorial current structure, a sharper thermocline, and elimination of the cold bias of the equatorial cold tongue all in the Pacific Ocean; reduced sea surface temperature (SST) and salinity biases along the North Atlantic Current path; and much smaller potential temperature and salinity biases in the near-surface Pacific Ocean. Other improvements include a global-mean SST that is more consistent with the present-day observations due to a different spinup procedure from that used in CCSM3. Despite these improvements, many of the biases present in CCSM3 still exist in CCSM4. A major concern continues to be the substantial heat content loss in the ocean during the preindustrial control simulation from which the 20C cases start. This heat loss largely reflects the top of the atmospheric model heat loss rate in the coupled system, and it essentially determines the abyssal ocean potential temperature biases in the 20C simulations. There is also a deep salty bias in all basins. As a result of this latter bias in the deep North Atlantic, the parameterized overflow waters cannot penetrate much deeper than in CCSM3.

High-resolution global climate modeling holds the promise of capturing planetary-scale climate modes and small-scale (regional and sometimes extreme) features simultaneously, including their mutual interaction. This paper discusses a new state-of-the-art high-resolution Community Earth System Model (CESM) simulation that was performed with these goals in mind. The atmospheric component was at 0.25 grid spacing, and ocean component at 0.1 . One hundred years of ''present-day'' simulation were completed. Major results were that annual mean sea surface temperature (SST) in the equatorial Pacific and ElNiño Southern Oscillation variability were well simulated compared to standard resolution models. Tropical and southern Atlantic SST also had much reduced bias compared to previous versions of the model. In addition, the high resolution of the model enabled small-scale features of the climate system to be represented, such as air-sea interaction over ocean frontal zones, mesoscale systems generated by the Rockies, and Tropical Cyclones. Associated single component runs and standard resolution coupled runs are used to help attribute the strengths and weaknesses of the fully coupled run. The high-resolution run employed 23,404 cores, costing 250 thousand processor-hours per simulated year and made about two simulated years per day on the NCAR-Wyoming supercomputer ''Yellowstone.''

The Argo Program has been implemented and sustained for almost two decades, as a global array of about 4000 profiling floats. Argo provides continuous observations of ocean temperature and salinity versus pressure, from the sea surface to 2000 dbar. The successful installation of the Argo array and its innovative data management system arose opportunistically from the combination of great scientific need and technological innovation. Through the data system, Argo provides fundamental physical observations with broad societally-valuable applications, built on the cost-efficient and robust technologies of autonomous profiling floats. Following recent advances in platform and sensor technologies, even greater opportunity exists now than 20 years ago to (i) improve Argo's global coverage and value beyond the original design, (ii) extend Argo to span the full ocean depth, (iii) add biogeochemical sensors for improved understanding of oceanic cycles of carbon, nutrients, and ecosystems, and (iv) consider experimental sensors that might be included in the future, for example to document the spatial and temporal patterns of ocean mixing. For Core Argo and each of these enhancements, the past, present, and future progression along a path from experimental deployments to regional pilot arrays to global implementation is described. The objective is to create a fully global, top-to-bottom, dynamically complete, and multidisciplinary Argo Program that will integrate seamlessly with satellite and with other in situ elements of the Global Ocean Observing System (Legler et al., 2015). The integrated system will deliver operational reanalysis and forecasting capability, and assessment of the state and variability of the climate system with respect to physical, biogeochemical, and ecosystems parameters. It will enable basic research of unprecedented breadth and magnitude, and a wealth of ocean-education and outreach opportunities.

O cean turbulence influences the transport of heat, freshwater, dissolved gases such as CO 2 , pollutants, and other tracers. It is central to understanding ocean energetics and reducing uncertainties in global circulation and simulations from climate models. The dissipation of turbulent energy in stratified water results in irreversible diapycnal (across density surfaces) mixing. Recent work has shown that the spatial and temporal inhomogeneity in diapycnal mixing may play a critical role in a variety of climate phenomena. Hence, a quantitative understanding of the physics that drive the distribution of diapycnal mixing in the ocean interior is fundamental to understanding the ocean's role in climate.Diapycnal mixing is very difficult to accurately parameterize in numerical ocean models for two reasons. The first one is due to the discrete representation of tracer advection in directions that are not perfectly aligned with isopycnals, which can result in numerically induced mixing from truncation errors that is larger than observed diapycnal mixing (Griffies et al. 2000;Ilıcak et al. 2012). The second reason is related to the intermittency of turbulence, which is generated by complex and chaotic motions that span a large space-time range. Furthermore, this mixing is driven by a wide range of processes with distinct governing physics that create a rich global geography [see MacKinnon et al. (2013c) for a review]. The difficulty is also related to the relatively sparse direct sampling of ocean mixing, whereby sophisticated ship-based measurements are generally required to accurately characterize ocean mixing processes. Nonetheless, we have sufficient evidence from theory, process models, laboratory experiments, and field measurements to conclude that away from ocean boundaries (atmosphere, ice, or the solid ocean bottom), diapycnal mixing is largely related to the breaking of internal gravity waves, which have a complex dynamical underpinning and associated geography. The study summarizes recent advances in our understanding of internal wave-driven turbulent mixing in the ocean interior and introduces new parameterizations for global climate ocean models and their climate impacts.

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The Community Climate System Model, version 4 (CCSM4) is used to assess the climate impact of windgenerated near-inertial waves (NIWs). Even with high-frequency coupling, CCSM4 underestimates the strength of NIWs, so that a parameterization for NIWs is developed and included into CCSM4. Numerous assumptions enter this parameterization, the core of which is that the NIW velocity signal is detected during the model integration, and amplified in the shear computation of the ocean surface boundary layer module. It is found that NIWs deepen the ocean mixed layer by up to 30%, but they contribute little to the ventilation and mixing of the ocean below the thermocline. However, the deepening of the tropical mixed layer by NIWs leads to a change in tropical sea surface temperature and precipitation. Atmospheric teleconnections then change the global sea level pressure fields so that the midlatitude westerlies become weaker. Unfortunately, the magnitude of the real air-sea flux of NIW energy is poorly constrained by observations; this makes the quantitative assessment of their climate impact rather uncertain. Thus, a major result of the present study is that because of its importance for global climate the uncertainty in the observed tropical NIW energy has to be reduced.