Vassily Lyutsarev

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model.

This paper presents the first mathematical model that attempts to represent the biology and behavior of all individual organisms globally, taking us a step closer to holistic ecological and conservation science founded on mechanistic predictions.

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

New details about natural and anthropogenic processes are continually added to models of the Earth system, anticipating that the increased realism will increase the accuracy of their predictions. However, perspectives differ about whether this approach will improve the value of the information the models provide to decision makers, scientists, and societies. The present bias toward increasing realism leads to a range of updated projections, but at the expense of uncertainty quantification and model tractability. This bias makes it difficult to quantify the uncertainty associated with the projections from any one model or to the distribution of projections from different models. This in turn limits the utility of climate model outputs for deriving useful information such as in the design of effective climate change mitigation and adaptation strategies or identifying and prioritizing sources of uncertainty for reduction. Here we argue that a new approach to model development is needed, focused on the delive...

Inferred support for disturbance‐recovery hypothesis of North Atlantic phytoplankton blooms

Analyses of satellite-derived chlorophyll data indicate that the phase of rapid phytoplankton population growth in the North Atlantic (the “spring bloom”) is actually initiated in the winter rather than the spring, contradicting Sverdrups critical depth hypothesis. An alternative disturbance-recovery hypothesis (DRH) has been proposed to explain this discrepancy, in which the rapid deepening of the mixed layer reduces zooplankton grazing rates sufficiently to initiate the bloom. We use Bayesian parameter inference on a simple Nutrient-Phytoplankton-Zooplankton (NPZ) model to investigate the DRH and also investigate how well the model can capture the multiyear and spatial dynamics of phytoplankton concentrations and population growth rates. Every parameter in our NPZ model was inferred as a probability distribution given empirical constraints, which provides a more objective method to identify a model parameterization given available empirical evidence, rather than fixing or tuning individual parameter values. Our model explains around 75% of variation in the seasonal dynamics of phytoplankton concentrations, 30% of variation in their population rates of change, and correctly predicts the phases of population growth and decline. Our parameter-inferred model supports the DRH, revealing the sustained reduction of grazing due to mixed-layer deepening as the driving mechanism behind bloom initiation, with the relaxation of nutrient limitation being another contributory mechanism. Our results also show that the continuation of the bloom is caused in part by the maintenance of phytoplankton concentrations below a level that can support positive zooplankton population growth. Our approach could be employed to formally assess alternative hypotheses for bloom formation.

Integrating and mining distributed environmental archives on Grids

The solar‐terrestrial physics distributed database for the ICSU World Data Centers, and the NCEP/NCAR climate re‐analysis data have been integrated into standard Grid environments using the OGSA‐DAI framework. A set of algorithms and software tools for distributed querying and mining environmental archives using the UNIDATA Common Data Model concepts has been developed. In addition, the toolkit enables querying the data using meaningful ‘human linguistic’ terms. Copyright