There is a lack of diversity amongst geoscience faculty. Therefore, many geoscience departments are taking steps to recruit and retain faculty from underrepresented groups. Here, we interview 19 geoscientists who identify as a member of an underrepresented race or gender who declined a tenure-track faculty job offer to investigate the factors influencing their decision. We find a range of key factors that influenced their decisions to accept or decline a position, including fit and resources, experiences during job interviews, negotiations and offers, family, geographic preferences, attention to DEI, personal identities, mentorship, hiring process, and teaching responsibilities. Despite existing recommendations for interventions to improve faculty diversity, many of the participants experienced hiring processes that did not follow these suggested best practices, suggesting that departments are not all aware of best hiring practices. Therefore, we leverage our results to provide actionable recommendations for improving the equity and effectiveness of faculty recruitment efforts. We find that institutions may doubly benefit from improving their culture: in addition to benefiting current members of the institution, it may also help with recruitment.

The atmospheric Green’s function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the "pattern effect." To better study this method, this paper presents a protocol for creating atmospheric Green’s functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere-only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming vs. cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green’s function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation.

The response of the Pacific Walker circulation (WC) to long-term warming remains uncertain. Here, we diagnose contributions to the WC response in comprehensive and idealized general circulation model (GCM) simulations. We find that the spread in WC response is substantial across both the Coupled Model Intercomparison Project (CMIP6) and the Atmospheric Model Intercomparison Project (AMIP) models, implicating differences in atmospheric models in the spread in projected WC strength. Using a moist static energy (MSE) budget, we evaluate the contributions to changes in the WC strength related to changes in gross moist stability (GMS), horizontal MSE advection, radiation, and surface fluxes. We find that the multimodel mean WC weakening is mostly related to changes in GMS and radiation. Furthermore, the spread in WC response is related to the spread in GMS and radiation responses. The GMS response is potentially sensitive to parameterized convective entrainment which can affect lapse rates and the depth of convection. We thus investigate the role of entrainment in setting the GMS response by varying the entrainment rate in an idealized GCM. The idealized GCM is run with a simplified Betts-Miller convection scheme, modified to represent entrainment. The weakening of the WC with warming in the idealized GCM is dampened when higher entrainment rates are used. However, the spread in GMS responses due to differing entrainment rates is much smaller than the spread in GMS responses across CMIP6 models. Therefore, further work is needed to understand the large spread in GMS responses across CMIP6 and AMIP models. 

The response of the Pacific Walker circulation (WC) to warming in both observations and simulations is uncertain. We diagnose contributions to the WC response in comprehensive and idealized general circulation model (GCM) simulations. We find that the spread in WC response is substantial across both the Coupled Model Intercomparison Project (CMIP6) and the Atmospheric Model Intercomparison Project (AMIP) models, implicating differences in atmospheric models in the spread in projected WC strength. Using a moist static energy (MSE) budget, we evaluate the contributions to changes in the WC strength related to changes in gross moist stability (GMS), horizontal MSE advection, radiation, and surface fluxes. We find that the multimodel mean WC weakening is mostly related to changes in GMS and radiation. Furthermore, different GMS responses can explain a substantial portion of the spread in WC responses. The GMS response is potentially sensitive to parameterized convective entrainment which can affect lapse rates and the depth of convection. We thus investigate the role of entrainment in setting the GMS response by varying the entrainment rate in an idealized GCM. The idealized GCM is run with a simplified Betts-Miller convection scheme, modified to represent entrainment. The WC weakening with warming in the idealized GCM is dampened when higher entrainment rates are used. However, the spread in GMS responses due to differing entrainment rates is much smaller than the spread in GMS responses across CMIP6 models. Therefore, further work is needed to understand the large spread in GMS responses across CMIP6 and AMIP models.