Geostatistical analysis of mesoscale spatial variability and error in SeaWiFS and MODIS/Aqua global ocean color data

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Oceans 123 (2018): 22–39, doi:10.1002/2017JC013023.Mesoscale (10–300 km, weeks to months) physical variability strongly modulates the structure and dynamics of planktonic marine ecosystems via both turbulent advection and environmental impacts upon biological rates. Using structure function analysis (geostatistics), we quantify the mesoscale biological signals within global 13 year SeaWiFS (1998–2010) and 8 year MODIS/Aqua (2003–2010) chlorophyll a ocean color data (Level-3, 9 km resolution). We present geographical distributions, seasonality, and interannual variability of key geostatistical parameters: unresolved variability or noise, resolved variability, and spatial range. Resolved variability is nearly identical for both instruments, indicating that geostatistical techniques isolate a robust measure of biophysical mesoscale variability largely independent of measurement platform. In contrast, unresolved variability in MODIS/Aqua is substantially lower than in SeaWiFS, especially in oligotrophic waters where previous analysis identified a problem for the SeaWiFS instrument likely due to sensor noise characteristics. Both records exhibit a statistically significant relationship between resolved mesoscale variability and the low-pass filtered chlorophyll field horizontal gradient magnitude, consistent with physical stirring acting on large-scale gradient as an important factor supporting observed mesoscale variability. Comparable horizontal length scales for variability are found from tracer-based scaling arguments and geostatistical decorrelation. Regional variations between these length scales may reflect scale dependence of biological mechanisms that also create variability directly at the mesoscale, for example, enhanced net phytoplankton growth in coastal and frontal upwelling and convective mixing regions. Global estimates of mesoscale biophysical variability provide an improved basis for evaluating higher resolution, coupled ecosystem-ocean general circulation models, and data assimilation.NASA's Ocean Biology and Biogeochemistry Grant Numbers: NNG05GG30G, NNG05GR34G, NNX14AM36G, NNX14AL86G, NNX15AE65G; Ocean Biology Processing Group (OBPG) at NASA's Goddard Space Flight Cente

Learning with (perceived) humans and computers: the role of agency beliefs in older adult collaborative learning.

Learning new skills and information becomes more challenging as people get older, due to a decline in cognitive abilities involved in learning. Learning collaboratively can provide benefits to learning in older age due to the social aspects of collaboration. Prior work has focused on the effects of interacting with another human, while the potential benefits of human-computer interaction are understudied. In the current study, 24 older adult participants (aged 65-80 years) completed an adapted version of the Map Task using a Wizard-of-Oz paradigm, in which participants were told that they were interacting with a human assistant or computer. The findings show that participants became faster across trials in both the Human and Computer conditions, though experienced a steeper reduction in time to complete in the Human learning condition. Despite taking longer, participants did not request additional input from the computer to assist their progress. When recalling their learned routes, participants recalled the route learned with the human more accurately both 1-hour and 1-week after the study session. Overall, participants engaged more with their human partner, and recall was more accurate when the information had been provided by the human, compared to the computer. Participants’ negative perceptions about the computer-provided information and about computers in general may have driven this effect. These findings can inform the future design of learning materials for older adults using natural or synthetic speech, as well as future research on collaborative learning in older age