Land surface hydrology in a general circulation model: global and regional fields needed for validation

For the last two decades most general circulation models (GCMs) have included some kind of surface hydrology submodel. The content of these submodels is becoming increasingly complex and realistic. It is still easy to identify defects in present treatments. Yet, to improve our ability to model the contribution of land hydrology to climate and climate change, we must be concerned not with just the surface hydrology submodel per se, but also with how it works in the overall context of the GCM.

Estimating the Energy Density of Fish: The Importance of Ontogeny

Ontogenetic patterns in the percent dry weight (%DW) and energy density (joules per gram of wet weight) were studied in the early life stages of the subtropical estuarine and marine gray snapper Lutjanus griseus and the warmtemperate estuarine and marine spotted seatrout Cynoscion nebulosus. The %DW was variable for individuals of both species but increased significantly through larval to juvenile stages (<20% for fish ,50 mm standard length to 20–30% for fish >50 mm). The lipid percentage, which was determined only for gray snapper, was also variable between individuals but showed significant increase with body size. Strong relationships between percent dry weight and energy density were evident for both species; however, the slopes of regressions were significantly lower than in general multispecies models, demonstrating the need for species- and stagespecific energy density data in bioenergetics models.

Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.

The annual course of precipitation over much of the United States: observed versus GCM simulation

General Circulation Models (GCMs) may be useful in estimating the ecological impacts of global climatic change. We analyzed seasonal weather patterns over the conterminous United States and determined that regional patterns of rainfall seasonality appear to control the distributions of the Nation's major biomes. These regional patterns were compared to the output from three GCMs for validation. The models appear to simulate the appropriate seasonal climates in the northern tier of states. However, the spatial extent of these regions is distorted. None of the models accurately portrayed rainfall seasonalities in the southern tier of states, where biomes are primarily influenced by the Bermuda High.

GCM-data intercomparison: the good news and the bad

EXTRACT (SEE PDF FOR FULL ABSTRACT): General circulation models (GCMs) are probably the most sophisticated theoretical tools we have to simulate possible climatic effects of increasing carbon dioxide and other greenhouse gases. ... As will be illustrated here using a variety of examples, although the models do simulate "reality" very well on the "grand" scale (e.g., global, hemispheric, zonal), substantial differences are more apparent as the scale is reduced to areas particularly relevant to regional planners. It is particularly important that workers more clearly recognize the potential dangers in relying too heavily on simple summary statistics such as averages estimated over large regional scales. Many shortcomings are apparent in the model simulations of the present climate, indicating that further model improvements are needed to achieve reliable regional and seasonal projections of the future climatic conditions.

On the use of combined compact scheme for numerical solution of the two-layer oceanic shallow water equations

Many types of oceanic physical phenomena have a wide range in both space and time. In general, simplified models, such as shallow water model, are used to describe these oceanic motions. The shallow water equations are widely applied in various oceanic and atmospheric extents. By using the two-layer shallow water equations, the stratification effects can be considered too. In this research, the sixth-order combined compact method is investigated and numerically implemented as a high-order method to solve the two-layer shallow water equations. The second-order centered, fourth-order compact and sixth-order super compact finite difference methods are also used to spatial differencing of the equations. The first part of the present work is devoted to accuracy assessment of the sixth-order super compact finite difference method (SCFDM) and the sixth-order combined compact finite difference method (CCFDM) for spatial differencing of the linearized two-layer shallow water equations on the Arakawa's A-E and Randall's Z numerical grids. Two general discrete dispersion relations on different numerical grids, for inertia-gravity and Rossby waves, are derived. These general relations can be used for evaluation of the performance of any desired numerical scheme. For both inertia-gravity and Rossby waves, minimum error generally occurs on Z grid using either the sixth-order SCFDM or CCFDM methods. For the Randall's Z grid, the sixth-order CCFDM exhibits a substantial improvement , for the frequency of the barotropic and baroclinic modes of the linear inertia-gravity waves of the two layer shallow water model, over the sixth-order SCFDM. For the Rossby waves, the sixth-order SCFDM shows improvement, for the barotropic and baroclinic modes, over the sixth-order CCFDM method except on Arakawa's C grid. In the second part of the present work, the sixth-order CCFDM method is used to solve the one-layer and two-layer shallow water equations in their nonlinear form. In one-layer model with periodic boundaries, the performance of the methods for mass conservation is compared. The results show high accuracy of the sixth-order CCFDM method to simulate a complex flow field. Furthermore, to evaluate the performance of the method in a non-periodic domain the sixth-order CCFDM is applied to spatial differencing of vorticity-divergence-mass representation of one-layer shallow water equations to solve a wind-driven current problem with no-slip boundary conditions. The results show good agreement with published works. Finally, the performance of different schemes for spatial differencing of two-layer shallow water equations on Z grid with periodic boundaries is investigated. Results illustrate the high accuracy of combined compact method.

A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example

We present a method to integrate environmental time series into stock assessment models and to test the significance of correlations between population processes and the environmental time series. Parameters that relate the environmental time series to population processes are included in the stock assessment model, and likelihood ratio tests are used to determine if the parameters improve the fit to the data significantly. Two approaches are considered to integrate the environmental relationship. In the environmental model, the population dynamics process (e.g. recruitment) is proportional to the environmental variable, whereas in the environmental model with process error it is proportional to the environmental variable, but the model allows an additional temporal variation (process error) constrained by a log-normal distribution. The methods are tested by using simulation analysis and compared to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. In the traditional method, the estimates of recruitment were provided by a model that allowed the recruitment only to have a temporal variation constrained by a log-normal distribution. We illustrate the methods by applying them to test the statistical significance of the correlation between sea-surface temperature (SST) and recruitment to the snapper (Pagrus auratus) stock in the Hauraki Gulf–Bay of Plenty, New Zealand. Simulation analyses indicated that the integrated approach with additional process error is superior to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. The results suggest that, for the snapper stock, recruitment is positively correlated with SST at the time of spawning.