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.

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.

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.