Barents Sea plankton production and controlling factors in a fluctuating climate

The Barents Sea and its marine ecosystem is exposed to many different processes related to the seasonal light variability, formation and melting of sea-ice, wind-induced mixing, and exchange of heat and nutrients with neighbouring ocean regions. A global model for the RCP4.5 scenario was downscaled, evaluated, and combined with a biophysical model to study how future variability and trends in temperature, sea-ice concentration, light, and wind-induced mixing potentially affect the lower trophic levels in the Barents Sea marine ecosystem. During the integration period (2010–2070), only a modest change in climate variables and biological production was found, compared to the inter-annual and decadal variability. The most prominent change was projected for the mid-2040s with a sudden decrease in biological production, largely controlled by covarying changes in heat inflow, wind, and sea-ice extent. The northernmost parts exhibited increased access to light during the productive season due to decreased sea-ice extent, leading to increased primary and secondary production in periods of low sea-ice concentrations. In the southern parts, variable access to nutrients as a function of wind-induced mixing and mixed layer depth were found to be the most dominating factors controlling variability in primary and secondary production.publishedVersio

Complexity in Prefix-Free Regular Languages

We examine deterministic and nondeterministic state complexities of regular operations on prefix-free languages. We strengthen several results by providing witness languages over smaller alphabets, usually as small as possible. We next provide the tight bounds on state complexity of symmetric difference, and deterministic and nondeterministic state complexity of difference and cyclic shift of prefix-free languages.Comment: In Proceedings DCFS 2010, arXiv:1008.127

Disclosing the truth: Are models better than observations?

The aphorism, ‘All models are wrong, but some models are useful’, originally referred to statistical models, but is now used for scientific models in general. When presenting results from a marine simulation model, this statement effectively stops discussions about the quality of the model, as there is always another observation to mismatch, and thereby another confirmation why the model cannot be trusted. It is common that observations are less challenged and are often viewed as a ‘gold standard’ for judging models, whereas proper interpretations and the true value of models are often overlooked. Models are not perfect, and there are many examples where models are used improperly to provide misleading answers with great confidence, but to what extent does an observation represent the truth? The precision of the observational gear may be high, but what about representativeness? The interpretation of observations is simply another model, but this time not coded in a computer language but rather formed by the individual observer. We submit that it would be more productive to initiate a process where the norm is that models and observations are joined to strengthen both. In the end, neither method is the goal, but both are useful tools for disclosing the truth. Biased views on either observational or modeling approaches would limit us from achieving this goal.Peer reviewe