Olfaction Contributes to Pelagic Navigation in a Coastal Shark.

How animals navigate the constantly moving and visually uniform pelagic realm, often along straight paths between distant sites, is an enduring mystery. The mechanisms enabling pelagic navigation in cartilaginous fishes are particularly understudied. We used shoreward navigation by leopard sharks (Triakis semifasciata) as a model system to test whether olfaction contributes to pelagic navigation. Leopard sharks were captured alongshore, transported 9 km offshore, released, and acoustically tracked for approximately 4 h each until the transmitter released. Eleven sharks were rendered anosmic (nares occluded with cotton wool soaked in petroleum jelly); fifteen were sham controls. Mean swimming depth was 28.7 m. On average, tracks of control sharks ended 62.6% closer to shore, following relatively straight paths that were significantly directed over spatial scales exceeding 1600 m. In contrast, tracks of anosmic sharks ended 37.2% closer to shore, following significantly more tortuous paths that approximated correlated random walks. These results held after swimming paths were adjusted for current drift. This is the first study to demonstrate experimentally that olfaction contributes to pelagic navigation in sharks, likely mediated by chemical gradients as has been hypothesized for birds. Given the similarities between the fluid three-dimensional chemical atmosphere and ocean, further research comparing swimming and flying animals may lead to a unifying paradigm explaining their extraordinary navigational abilities

Ensembles of AGCM Two-Tier Predictions and Simulations of the Circulation Anomalies during Winter 1997–98

The impact of sea surface temperature (SST) anomalies on the extratropical circulation during the El Niño winter of 1997–98 is studied through atmospheric general circulation model (AGCM) integrations. The model's midlatitude response is found to be very robust, of the correct amplitude, and to have a fairly realistic spatial structure. The sensitivity of the results to different aspects of the anomalous distributions of SST is analyzed. It is found that the extratropical circulation in the North Pacific–North American sector is significantly different if SST anomalies over the Indian Ocean are included. Using a comparison of observed and simulated 200-hPa streamfunction anomalies, it is argued that the modeled midlatitude impact of Indian Ocean SST anomalies is largely realistic. However, while the local sensitivity of the atmosphere to small differences in SST anomalies in the tropical Pacific can be substantial, the remote sensitivity in midlatitudes is small. Consistently, there is little difference between the simulated extratropical circulation anomalies obtained using SSTs predicted by the National Centers for Environmental Prediction in October 1997 and those obtained using observed tropical Pacific SSTs. Neither is there any detectable atmospheric signal associated with SST anomalies over the North Pacific. Analyses of the results presented here suggest that the influence of SST anomalies in the Pacific and Indian Oceans during the selected ENSO event can be interpreted as the quasi-linear superposition of Rossby wave trains emanating from the subtropics of each ocean. An inspection of intraseasonal weather regimes suggests that the influence of tropical SST anomalies can also be described as a shift in the frequency of occurrence of the model's modes of intrinsic variability and a change in their amplitude. These findings suggest the potential utility of SST forecasts for the tropical Indian Ocean

Simulations of the Atmospheric Response to South Atlantic Sea Surface Temperature Anomalies

The sensitivity of the atmospheric circulation to sea surface temperature (SST) anomalies in the tropical and subtropical South Atlantic Ocean is studied by means of simulations with an atmospheric general circulation model (GCM). Two types of prescribed SST anomalies are used, motivated by previous analyses of data. The first occurs during austral summers in association with a strengthening of the South Atlantic convergence zone (SACZ) and consists of cold SST anomalies over the subtropical South Atlantic. The second is the leading seasonally varying empirical orthogonal function of SST, consisting of warm basin-scale anomalies with maximum amplitude in the subtropics during January–March and at the equator in June. An ensemble of about 10 seasonal simulations is made using each type of anomaly, focusing on the January–March period in the first case and the January–June seasonal evolution in the second. During January–March both experiments yield a statistically significant baroclinic response over the subtropical Atlantic with dipolar SACZ-like anomalies. Some evidence of positive feedback is found. The response is shown to be fairly similar in pattern as well as amplitude to the linear regression of observed interannual low-level wind anomalies with subtropical SST anomalies. However, in the first experiment with cold SST anomalies, the simulated response contrasts with the leading interannual mode of observed SACZ variability. Warm basin-scale anomalies are found to have their largest impact during boreal summer, with a strong statistically significant equatorial baroclinic response and positive rainfall anomalies over the equatorial ocean. The latter do not extend appreciably into the adjacent continents, although there are significant positive rainfall anomalies over the Sahel in April–June and negative anomalies over the western Indian Ocean. In the upper troposphere, a statistically significant wave train extends southwestward to southern South America and northeastward to Europe in April–June, while there is some linkage between the tropically and subtropically forced responses during January–March

South Atlantic Variability Arising from Air–Sea Coupling: Local Mechanisms and Tropical–Subtropical Interactions

Interannual variability in the southern and equatorial Atlantic is investigated using an atmospheric general circulation model (AGCM) coupled to a slab ocean model (SOM) in the Atlantic in order to isolate features of air–sea interactions particular to this basin. Simulated covariability between sea surface temperatures (SSTs) and atmosphere is very similar to the observed non-ENSO-related covariations in both spatial structures and time scales. The leading simulated empirical coupled mode resembles the zonal mode in the tropical Atlantic, despite the lack of ocean dynamics, and is associated with baroclinic atmospheric anomalies in the Tropics and a Rossby wave train extending to the extratropics, suggesting an atmospheric response to tropical SST forcing. The second non-ENSO mode is the subtropical dipole in the SST with a mainly equivalent barotropic atmospheric anomaly centered on the subtropical high and associated with a midlatitude wave train, consistent with atmospheric forcing of the subtropical SST. The power spectrum of the tropical mode in both simulation and observation is red with two major interannual peaks near 5 and 2 yr. The quasi-biennial component exhibits a progression between the subtropics and the Tropics. It is phase locked to the seasonal cycle and owes its existence to the imbalances between SST–evaporation and SST–shortwave radiation feedbacks. These feedbacks are found to be reversed between the western and eastern South Atlantic, associated with the dominant role of deep convection in the west and that of shallow clouds in the east. A correct representation of tropical–extratropical interactions and of deep and shallow clouds may thus be crucial to the simulation of realistic interannual variability in the southern and tropical Atlantic

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Development, implementation, and validation of a California coastal ocean modeling, data assimilation, and forecasting system

A three-dimensional, near real-time data-assimilative modeling system for the California coastal ocean is presented. The system consists of a Regional Ocean Modeling System (ROMS) forced by the North American Mesoscale Forecast System (NAM). The ocean model has a horizontal resolution of approximately three kilometers and utilizes a multi-scale three-dimensional variational (3DVAR) data assimilation methodology. The system is run in near real-time to produce a nowcast every six hours and a 72-hour forecast every day. The performance of this nowcast system is presented using results from a six-year period of 2009–2015. The ROMS results are first compared with the assimilated data as a consistency check. RMS differences in observed satellite infrared sea surface temperatures (SST) and vertical profiles of temperature between observations and ROMS nowcasts were found to be mostly less than 0.5 °C, while the RMS differences in vertical profiles of salinity between observations and ROMS nowcasts were found to be 0.09 or less. The RMS differences in SST show a distinct seasonal cycle that mirrors the number of observations available: the nowcast is less skillful with larger RMS differences during the summer months when there are less infrared SST observations due to the presence of low-level clouds. The larger differences during summer were found primarily along the northern and central coasts in upwelling regions where strong gradients exist between colder upwelled waters nearshore and warmer offshore waters. RMS differences between HF radar surface current observations and ROMS nowcasts were approximately 7–8 cm s−1, which is about 30% of the time mean current speeds in this region. The RMS differences in sea surface height (SSH) between the AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic) altimetric satellite observations and ROMS nowcasts were about 2 cm. In addition, the system realistically reproduces the interannual variability in temperatures at the M1 mooring (122.03°W, 36.75°N) in Monterey Bay, including the strong warming of the California coastal ocean during 2014. The ROMS nowcasts were then validated against independent observations. A comparison of the ROMS nowcast with independent profile observations of temperature and salinity shows RMS differences of 0.7 to 0.92 °C and 0.13 to 0.17, which are larger (by up to a factor of 2) than the differences found in the comparisons with assimilated data. Validation of the depth-averaged currents derived from Spray gliders shows that the flow patterns associated with California Current and California Undercurrent/Davidson current systems and their seasonal variations are qualitatively reproduced by the ROMS modeling system. Lastly, the impact of two recent upgrades to the system is quantified. Switching the lateral boundary conditions from a U.S. west coast regional model to the global HYCOM (HYbrid Coordinate Ocean Model) model results in an improvement in the simulation of the seasonal and interannual variations in the SSH, especially south of Pt. Conception (120.47°W, 34.45°N). The assimilation of altimetric satellite SSH data also results in an improvement in the model surface currents when compared to independent surface drifter observations

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Understanding and predicting seasonal-to-interannual fluctuations in California precipitation using an atmospheric general circulation model

The water supply in California is subject to large variations on a variety of timescales ranging from intraseasonal to decadal. Interannual variations were the focus of the research undertaken in this project. The primary source of water in California is precipitation associated with winter storms originating over the North Pacific Ocean. Thus, the variability in the water supply is ultimately linked to variations in the precipitation. It is known that a significant amount of the interannual variability in precipitation is related to variations in sea surface temperatures (SSTs) in the tropical eastern Pacific Ocean - El Niño and La Niña events. However, the response in California precipitation varies from event to event, in part because the SST anomalies do not evolve in the same way during each event. In addition, extreme events such as the flood of January 1997 can occur even when tropical Pacific SST anomalies are weak, suggesting that mechanisms other than El Niño/La Niña forcing can produce seasonal to interannual variations in California precipitation. The natural variability of the Pacific storm track is one such possible mechanism. These storm track variations can modulate the frequency, strength, and location of landfall of winter storms

8 Performance Analysis and Optimization on a Parallel Atmospheric General Circulation Model Code

An analysis is presented of the primary factors influencing the performance of a parallel implementation of the UCLA atmospheric general circulation model (AGCM) on distributed-memory, massively parallel computer systems. Several modifications to the original parallel AGCM code aimed at improving its numerical efficiency, load-balance and single-node code performance are discussed, The impact of these optimization strategies on the performance on two of the state-ofthe-art parallel computers, the Intel Paragon and Cray T3D, is presented and analyzed. It is found that implementation of a load-balanced ITT algorithm results in a reduction in overall execution time of approximately 45 % compared to the original convolution-based algorithm, Preliminary results of the application of a load-balancing scheme for the Physics part of the AGCM code suggest additional reductions in execution time of 10-15 % can be achieved. Finally, several strategies for improving the single-node performance of the code are presented, and the results obtained thus far suggest reductions in execution time in the range of 35-45 % are possible. 1