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This work-in-progress paper explores links between engineering students' environmental awareness and their intended environmental behavior at different levels in a prominent HBCU. Through extensive surveys developed as part of this project, students' higher-level behavior, manifested by their willingness and preparedness to pursue careers in the industries developing sustainable resources, has been explored. To maximize the high-level behavior and sustainability competencies, a pedagogical system with a comprehensive pool of interventions has also been developed and implemented in a senior-level mechanical engineering course. In this paper, we report the initial survey response data and details of the intervention strategies, which are intended to develop scalable educational approaches and guidelines for building high-level environmental behavior in the next-generation diverse renewable energy workforce. 

This work-in-progress paper explores links between engineering students' environmental awareness and their intended environmental behavior at different levels in a prominent HBCU. Through extensive surveys developed as part of this project, students' higher-level behavior, manifested by their willingness and preparedness to pursue careers in the industries developing sustainable resources, has been explored. To maximize the high-level behavior and sustainability competencies, a pedagogical system with a comprehensive pool of interventions has also been developed and implemented in a senior-level mechanical engineering course. In this paper, we report the initial survey response data and details of the intervention strategies, which are intended to develop scalable educational approaches and guidelines for building high-level environmental behavior in the next-generation diverse renewable energy workforce. 

Abstract Current and previous thermospheric remote sensing missions use N₂Lyman‐Birge‐Hopfield (LBH) band dayglow emission measurements to retrieve line‐of‐sight thermospheric composition and temperature. The precision of thermospheric composition and temperature retrieved from observations depends on the uncertainty in the relative LBH vibrational populations. In the laboratory, electron impact induced LBH emission measurements have shown that the relative vibrational populations change with gas pressure. However, it is not fully understood how these populations change for dayglow observations where the emissions that contribute to the observations vary with solar illumination and line‐of‐sight geometry. We quantify the relative vibrational populations as a function of solar zenith angle (SZA) and tangent altitude using Global‐scale Observations of Limb and Disk mission's LBH dayglow observations. We find that, while some lower vibrational levels show potential enhancement with increasing pressure (decreasing altitude), in general, they do not change significantly with SZA or tangent altitude for dayglow observations. The vibrational populations can thus be assumed as fixed parameters when retrieving neutral disk temperatures from remotely sensed LBH dayglow observations. 

Previous work identified an anthropogenic fingerprint pattern in 𝑇AC (𝑥, 𝑡), the amplitude of the seasonal cycle of mid- to upper tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite 𝑇AC(𝑥,𝑡) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their 𝑇AC (𝑥, 𝑡) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3-4) inter-model and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal 𝑇AC(𝑥,𝑡) variability associated with the Atlantic Multidecadal Oscillation and the El Niño~Southern Oscillation. The robustness of the seasonal cycle D&A results shown here, taken together with the evidence fromidealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced𝑇AC(𝑥,𝑡) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease. 

We compare atmospheric temperature changes in satellite data and in older and newer multi-model and single-model ensembles performed under phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In the lower stratosphere, multi-decadal stratospheric cooling during the period of strong ozone depletion is smaller in newer CMIP6 simulations than in CMIP5 or satellite data. In the troposphere, however, despite differences in the forcings and climate sensitivity of the CMIP5 and CMIP6 ensembles, their ensemble-average global warming over the satellite era is remarkably similar. We also examine four well-understood properties of tropical behavior governed by basic physical processes. The first three properties are ratios between trends in water vapor (WV) and trends in sea surface temperature (SST), the temperature of the lower troposphere (TLT), and the temperature of the mid- to upper troposphere (TMT). The fourth property is the ratio between TMT and SST trends. All four trend ratios are tightly constrained in CMIP simulations. Observed ratios diverge markedly when calculated with SST, TLT, and TMT trends produced by different groups. Observed data sets with larger warming of the tropical ocean surface and tropical troposphere yield atmospheric moistening that is closer to model results. For the TMT/SST ratio, model-data consistency depends on the selected combination of observed data sets used to estimate TMT and SST trends. If model expectations of these four covariance relationships are realistic, one interpretation of our findings is that they reflect a systematic low bias in satellite tropospheric temperature trends. Alternately, the observed atmospheric moistening signal may be overestimated. Given the large structural uncertainties in observed tropical TMT and SST trends, and because satellite WV data are available from one group only, it is difficult to determine which interpretation is more credible. Nevertheless, our analysis illustrates the diagnostic power of simultaneously considering multiple complementary variables and points towards possible problems with certain observed data sets.