The potential for sea ice-albedo feedback to give rise to nonlinear climate change in the Arctic Ocean – defined as a nonlinear relationship between polar and global temperature change or, equivalently, a time-varying polar amplification – is explored in IPCC AR4 climate models. Five models supplying SRES A1B ensembles for the 21 st century are examined and very linear relationships are found between polar and global temperatures (indicating linear Arctic Ocean climate change), and between polar temperature and albedo (the potential source of nonlinearity). Two of the climate models have Arctic Ocean simulations that become annually sea ice-free under the stronger CO 2 increase to quadrupling forcing. Both of these runs show increases in polar amplification at polar temperatures above-5 o C and one exhibits heat budget changes that are consistent with the small ice cap instability of simple energy balance models. Both models show linear warming up to a polar temperature of-5 o C, well above the disappearance of their September ice covers at about-9 o C. Below-5 o C, surface albedo decreases smoothly as reductions move, progressively, to earlier parts of the sunlit period. Atmospheric heat transport exerts a strong cooling effect during the transition to annually ice-free conditions. Specialized experiments with atmosphere and coupled models show that the main damping mechanism for sea ice region surface temperature is reduced upward heat flux through the adjacent ice-free oceans resulting in reduced atmospheric heat transport into the region.

Geophysical fluid dynamics.

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Assessing the Impact

United States. National Oceanic and Atmospheric Administration (Postdoctoral Fellowship Program)

Physically-based Assessment of Hurricane Surge Threat under Climate Change

The hypothesis that physical lagoon characteristics and variability depend on the channel connecting the lagoon to the adjacent coastal ocean is evaluated. The geographical, hydrological, and oceanographic characteristics of 10 lagoon systems are described and analyzed; these oceanographic features are utilized to classify the lagoon systems. Choked lagoons (Laguna Joyuda, Coorong, Lake St.Lucia, Gippsland Lakes, Lake Songkla/Thale Luang/Thale Noi, and Lagoa dos Patos) are prevalent on coasts with high wave energy and low tidal range; restricted lagoons (Lake Pontchartrain and Laguna de Terminos) are located on low/medium wave energy coasts with a low tidal range; and leaky lagoons (Mississippi Sound and Belize Lagoon/Chetumal Bay) are connected to the ocean by wide tidal passes that transmit oceanic effects into the lagoon with a minimum of resistance. The data support the hypothesis that the nature of the connecting channel controls system functions.

Comparative oceanography of coastal lagoons

Abstract The state-of-the-art in oil spill modeling is summarized, focusing primarily on the years from 1990 to the present. All models seek to describe the key physical and chemical processes that transport and weather the oil on and in the sea. Current insights into the mechanisms of these processes and the availability of algorithms for describing and predicting process rates are discussed. Advances are noted in the areas of advection, spreading, evaporation, dispersion, emulsification, and interactions with ice and shorelines. Knowledge of the relationship between oil properties, and oil weathering and fate, and the development of models for the evaluation of oil spill response strategies are summarized. Specific models are used as examples where appropriate. Future directions in these and other areas are indicated

Oil spill modeling toward the close of the 20th.century: overview of state of the art

Process-based models have been widely used for storm surge predictions, but their high computational demand is a major drawback in some applications such as rapid forecasting. Few efforts have been made to employ previous databases of synthetic/real storms and provide more efficient surge predictions (e.g. using storm similarity of an individual storm to those in the database). Here, we develop an alternative efficient and robust artificial intelligent model, which predicts the peak storm surge using the tropical storm parameters: central pressure, radius to maximum winds, forward velocity, and storm track. The US Army Corp of Engineers, North Atlantic Comprehensive Coastal Study, has recently performed numerical simulations of 1050 synthetic tropical storms, which statistically represent tropical storms, using a coupled high resolution wave–surge modeling system for the east coast of the US, from Cape Hatteras to the Canadian border. This study has provided an unprecedented dataset which can be used to train artificial intelligence models for surge prediction in those areas. While numerical simulation of a storm surge at this scale and resolution (over 6 million elements scaling from 20 m to more than 100 km) is extremely expensive, the artificial intelligence takes the advantage of the previous simulations, and effectively learns the relationship between storm parameters representing storm forcing and surge. The artificial neural network method which was used for this study, was shown to outperform support vector machine for extreme storms. ANN model, which is based on a neurobiological analogy, can be conveniently developed, retrained by new data, and is nonparametric. The AI model, which was developed for Rhode Island, was validated using a set of randomly selected synthetic storms as well as real tropical storms in this region. The model performance was found satisfactory with root-mean-square error of <35 cm for observed and synthetic storms. It was also shown that it is not possible to develop a reliable artificial intelligence model for this region using a limited number of data (e.g. 200 storms), which is usually available in historical records.

An efficient artificial intelligence model for prediction of tropical storm surge

A finite difference model is presented for viscous two dimensional flow of a uniform stream past an oscillating cylinder. A noninertial coordinate transformation is used so that the grid mesh remains fixed relative to the accelerating cylinder. Three types of cylinder motion are considered: oscillation in a still fluid, oscillation parallel to a moving stream, and oscillation transverse to a moving stream. Computations are made for Reynolds numbers between 1 and 100 and amplitude-to-diameter ratios from 0.1 to 2.0. The computed results correctly predict the lock-in or wake-capture phenomenon which occurs when cylinder oscillation is near the natural vortex shedding frequency. Drag, lift, and inertia effects are extracted from the numerical results. Detailed computations at a Reynolds number of 80 are shown to be in quantitative agreement with available experimental data for oscillating cylinders.

Numerical Solution for Laminar Two Dimensional Flow About a Cylinder Oscillating in a Uniform Stream

A three-dimensional numerical hydrodynamic model was used to study the tides and certain aspects of the wind-forced flow in Narragansett Bay. The governing equations are solved using the Galerkin-spectral method in the vertical and explicit finite differences in the horizontal and time. A split-mode formulation is used to minimize computational time. Simulations of the M 2 tide gave average errors of less than 2% in amplitude and 2° in phase when compared with observations at 12 tidal-height stations within the Bay. An investigation of the Bay's response to steady, spatially uniform wind forcing indicated that the non-linear interaction between the tidal and wind induced motions strongly affected the predicted steady state current pattern. Calculation of the steady-state wind-induced transports showed that a significant exchange exists between the West Passage, East Passage and Sakonnet River. The dependence of the exchange on the wind magnitude and direction and on the choice of vertical eddy viscosity was investigated. A comparison between modeled results and measurements of current at three locations in the Bay indicated that the model is capable of reproducing some of the features of the wind-induced flow.

Numerical simulations of the tidal- and wind-driven circulation in Narragansett Bay

The flushing time of the Providence River was estimated using three different data sets and three different methodologies Dye concentrations were measured following instantaneous dye releases during wet weather experiments performed by the Narragansett Bay Project between October 1988 and June 1989 These data were analyzed to obtain flushing time estimates Salinity measurements collected during the Sinbadd (Sampling In Narragansett Bay All During the Day) cruises, Spray (Sampling the Providence River All Year) cruises and wet weather experiments were used with the fraction of fresh water method and box model to calculate flushing time The Sinbadd cruises performed 4 seasonal surveys at 22 stations in Narragansett Bay during 1986 to obtain a view of the whole Narragansett Bay with respect to the concentrations of nutrients and trace metals The Spray cruises collected data in the Providence River at 10 stations to determine the relationship of nutrients and trace metals concentrations in the Seekonk and Providence rivers as a function of point source inputs Based on the flushing time estimates, an exponential relationship between freshwater inflow and flushing time was developed (correlation coefficient of 0826) The flushing time ranged from 08 d at high (90 m3 m−1) freshwater inflows to 44 d at low (20 m3 s−1) freshwater inflows The average flushing time of the Providence River was estimated as 25 d for the mean freshwater inflow of 423 m3 s−1

Malcolm L. Spaulding

Papers

An efficient artificial intelligence model for prediction of tropical storm surge

Numerical Solution for Laminar Two Dimensional Flow About a Cylinder Oscillating in a Uniform Stream

Numerical simulations of the tidal- and wind-driven circulation in Narragansett Bay

Flushing Times for the Providence River Based on Tracer Experiments

Role of Hurricane Wind Models in Accurate Simulation of Storm Surge and Waves