Optimizing PAT data transmission: assessing the accuracy of temperature summary data to estimate environmental conditions

Pop-up archival tags (PAT) provide summary and high-resolution time series data at predefined temporal intervals. The limited battery capabilities of PATs often restrict the transmission success and thus temporal coverage of both data products. While summary data are usually less affected by this problem, as a result of its lower size, it might be less informative. We here investigate the accuracy and feasibility of using temperature at depth summary data provided by PATs to describe encountered oceanographic conditions. Interpolated temperature at depth summary data was found to provide accurate estimates of three major thermal water column structure indicators: thermocline depth, stratification and ocean heat content. Such indicators are useful for the interpretation of the tagged animal's horizontal and vertical behaviour. The accuracy of these indicators was found to be particularly sensitive to the number of data points available in the first 100 m, which in turn depends on the vertical behaviour of the tagged animal. Based on our results, we recommend the use of temperature at depth summary data as opposed to temperature time series data for PAT studies; doing so during the tag programming will help to maximize the amount of transmitted time series data for other key data types such as light levels and depth.

Uncertainties in projecting climate-change impacts in marine ecosystems

Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale. We conclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections.