Description and Evaluation of the specified-dynamics experiment in the Chemistry-Climate Model Initiative

We provide an overview of the REF-C1SD specified-dynamics experiment that was conducted as part of phase 1 of the Chemistry-Climate Model Initiative (CCMI). The REF-C1SD experiment, which consisted of mainly nudged general circulation models (GCMs) constrained with (re)analysis fields, was designed to examine the influence of the large-scale circulation on past trends in atmospheric composition. The REF-C1SD simulations were produced across various model frameworks and are evaluated in terms of how well they represent different measures of the dynamical and transport circulations. In the troposphere there are large (∼40 %) differences in the climatological mean distributions, seasonal cycle amplitude, and trends of the meridional and vertical winds. In the stratosphere there are similarly large (∼50 %) differences in the magnitude, trends and seasonal cycle amplitude of the transformed Eulerian mean circulation and among various chemical and idealized tracers. At the same time, interannual variations in nearly all quantities are very well represented, compared to the underlying reanalyses. We show that the differences in magnitude, trends and seasonal cycle are not related to the use of different reanalysis products; rather, we show they are associated with how the simulations were implemented, by which we refer both to how the large-scale flow was prescribed and to biases in the underlying free-running models. In most cases these differences are shown to be as large or even larger than the differences exhibited by free-running simulations produced using the exact same models, which are also shown to be more dynamically consistent. Overall, our results suggest that care must be taken when using specified-dynamics simulations to examine the influence of large-scale dynamics on composition

Getting Under the Hood: How and for Whom Does Increasing Course Structure Work?

At the college level, the effectiveness of active-learning interventions is typically measured at the broadest scales: the achievement or retention of all students in a course. Coarse-grained measures like these cannot inform instructors about an intervention’s relative effectiveness for the different student populations in their classrooms or about the proximate factors responsible for the observed changes in student achievement. In this study, we disaggregate student data by racial/ethnic groups and first-generation status to identify whether a particular intervention—increased course structure—works better for particular populations of students. We also explore possible factors that may mediate the observed changes in student achievement. We found that a “moderate-structure ” intervention increased course performance for all student populations, but worked disproportionately well for black students—halving the black–white achievement gap—and first-generation students—closing the achievement gap with continuing-generation students. We also found that students consistently reported completing the assigned readings more frequently, spending more time studying for class, and feeling an increased sense of community in the moderate-structure course. These changes imply that increased course structure improves student achievement at least partially through increasing student use of distributed learning and creating a more interdependent classroom community

A Social Capital Perspective on the Mentoring of Undergraduate Life Science Researchers: An Empirical Study of Undergraduate–Postgraduate–Faculty Triads

Undergraduate researchers at research universities are often mentored by graduate students or postdoctoral researchers (referred to collectively as “postgraduates”) and faculty, creating a mentoring triad structure. Triads differ based on whether the undergraduate, postgraduate, and faculty member interact with one another about the undergraduate’s research. Using a social capital theory framework, we hypothesized that different triad structures provide undergraduates with varying resources (e.g., information, advice, psychosocial support) from the postgraduates and/or faculty, which would affect the undergraduates’ research outcomes. To test this, we collected data from a national sample of undergraduate life science researchers about their mentoring triad structure and a range of outcomes associated with research experiences, such as perceived gains in their abilities to think and work like scientists, science identity, and intentions to enroll in a PhD program. Undergraduates mentored by postgraduates alone reported positive outcomes, indicating that postgraduates can be effective mentors. However, undergraduates who interacted directly with faculty realized greater outcomes, suggesting that faculty interaction is important for undergraduates to realize the full benefits of research. The “closed triad,” in which undergraduates, postgraduates, and faculty all interact directly, appeared to be uniquely beneficial; these undergraduates reported the highest gains in thinking and working like a scientist