A global glacial ocean state estimate constrained by upper-ocean temperature proxies

Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 31 (2018): 8059-8079, doi:10.1175/JCLI-D-17-0769.1.We use the method of least squares with Lagrange multipliers to fit an ocean general circulation model to the Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface (MARGO) estimate of near sea surface temperature (NSST) at the Last Glacial Maximum (LGM; circa 23–19 thousand years ago). Compared to a modern simulation, the resulting global, last-glacial ocean state estimate, which fits the MARGO data within uncertainties in a free-running coupled ocean–sea ice simulation, has global-mean NSSTs that are 2°C lower and greater sea ice extent in all seasons in both the Northern and Southern Hemispheres. Increased brine rejection by sea ice formation in the Southern Ocean contributes to a stronger abyssal stratification set principally by salinity, qualitatively consistent with pore fluid measurements. The upper cell of the glacial Atlantic overturning circulation is deeper and stronger. Dye release experiments show similar distributions of Southern Ocean source waters in the glacial and modern western Atlantic, suggesting that LGM NSST data do not require a major reorganization of abyssal water masses. Outstanding challenges in reconstructing LGM ocean conditions include reducing effects from model biases and finding computationally efficient ways to incorporate abyssal tracers in global circulation inversions. Progress will be aided by the development of coupled ocean–atmosphere–ice inverse models, by improving high-latitude model processes that connect the upper and abyssal oceans, and by the collection of additional paleoclimate observations.DEA was supported by a NSF Graduate Research Fellowship and NSF Grant OCE-1060735. OM acknowledges support from the NSF. GF was supported by NASA Award 1553749 and Simons Foundation Award 549931

Blending for student engagement: lessons learned for the MOOCs and beyond

The purpose of this ongoing, three-year action research study is to explore the digital challenges of student engagement in higher education within the experimental platform of blended learning. Research questions examine the role of digital innovation in supporting diverse learners, as well as building meaningful connections with technology for undergraduate teacher education students. Results from qualitative data collected through instructor journals and field notes and student mid-term and exit surveys during year one, indicate blended learning can be effective for modelling how to use technology to shift learners towards more active agency. The immediacy of the localised university classroom delivered a viable research setting for digital experimentation, while providing a significant lived experience for undergraduates to springboard their future technological practices with K–12 students. Four pedagogical opportunities for digital intentionality in virtual spaces emerged during data analysis and are shared as considerations for future innovation: (1) designing digital resources, (2) scaffolding student learning, (3) learner customisation, and (4) promoting the lived experience. Lessons learned could be effective in helping develop higher quality educational experiences for on-campus students, as well as scaffolding greater engagement in online formats involving more global populations (e.g., massive online open courses – MOOCs)

Spatiotemporal patterns of small for gestational age and low birth weight births and associations with land use and socioeconomic status

In addition to small for gestational age (SGA) and low birth weight at term (LBWT), critically ill cases of SGA/LBWT are significant events from outcomes and economic perspectives that require further understanding of risk factors. We aimed to assess the spatiotemporal distribution of locations where there were consistently higher numbers of critically ill SGA/LBWT (hot spots) in comparison with all SGA/LBWT and all births. We focused on Edmonton (2008-2010) and Calgary (2006-2010), Alberta, and used a geographical information system to apply emerging hot spot analysis, as a new approach for understanding SGA, LBWT, and the critically ill counterparts (ciSGA or ciLBWT). We also compared the resulting aggregated categorical patterns with proportions of land use and socioeconomic status (SES) using Spearman correlation and logistic regression. There was an overall increasing trend in all space-time clusters. Whole period emerging hot spot patterns among births and SGA generally coincided, but SGA with ciSGA and LBWT with ciLBWT did not. Regression coefficients were highest for low SES with SGA and LBWT, but not with ciSGA and ciLBWT. Open areas and industrial land use were most associated with ciLBWT but not with ciSGA, SGA, or LBWT. Differences in the space-time hot spot patterns and the associations with ciSGA and ciLBWT indicate further need to research the interplay of maternal and environmental influences. We demonstrated the novel application of emerging hot spot analysis for small newborns and spatially related them to the surrounding environment

Effect of motor vehicle emissions on respiratory health in an urban area.

Motor vehicles emit particulate matter < 2.5 microm in diameter (PM(2.5)), and as a result, PM(2.5) concentrations tend to be elevated near busy streets. Studies of the relationship between motor vehicle emissions and respiratory health are generally limited by difficulties in exposure assessment. We developed a refined exposure model and implemented it using a geographic information system to estimate the average daily census enumeration area (EA) exposure to PM(2.5). Southeast Toronto, the study area, includes 334 EAs and covers 16 km(2) of urban area. We used hospital admission diagnostic codes from 1990 to 1992 to measure respiratory and genitourinary conditions. We assessed the effect of EA exposure on hospital admissions using a Poisson mixed-effects model and examined the spatial distributions of variables. Exposure to PM(2.5) has a significant effect on admission rates for a subset of respiratory diagnoses (asthma, bronchitis, chronic obstructive pulmonary disease, pneumonia, upper respiratory tract infection), with a relative risk of 1.24 (95% confidence interval, 1.05-1.45) for a log(10) increase in exposure. We noted a weaker effect of exposure on hospitalization for all respiratory conditions, and no effect on hospitalization for nonrespiratory conditions

Searching for the elusive aggregation effect: evidence from statistical simulations

The past few years have seen a resurging interest in the modifiable areal unit problem, or aggregation effects. The new evidence, however, both supports and conflicts with previous work. This paper represents the first stage in a series of numerical experiments designed to explore the nature and extent of scale and zonation effects. Results from a series of carefully controlled statistical simulations are reported. It is concluded that there definitely are aggregation effects separate from effects that can be attributed to changing the definition of the spatial process. These effects, however, vary with the statistic calculated. Means and variances are resistant to aggregation effects, whereas regression coefficients and correlation statistics exhibit dramatic effects. In summary, the world of spatial analysis as it relates to the modifiable areal unit problem is not entirely well-behaved, but neither is it completely random and ill-defined