Margaret L. Duffy

The Community Earth System Model version 2 (CESM2) has a higher equilibrium climate sensitivity (ECS) than previous versions of CESM and many other Coupled Model Intercomparison Project models. Relatedly, CESM2 simulates too‐cold ice‐age and too‐hot warm paleoclimates. An inappropriate ice number limiter in the CESM2 microphysics scheme was discovered, and some simulations indicate that the high ECS may be partially attributable to this inappropriate limiter. In light of those findings, we seek to provide users of CESM2 guidance on the fitness of CESM2 for a variety of applications. We find that despite concerns about its climate sensitivity and simulations of past climates, the transient climate response in CESM2 is moderate relative to the CMIP6 ensemble and robust across different versions of CESM. The changes made between CESM1 and CESM2 and the fixes to the microphysical issues of CESM2 have little impact on its simulated 20th and 21st century climates under SSP3–7.0. As a result, the simulated 20th and 21st century climates of CESM2 fall well within the range of the CMIP6 ensemble and agree well with observations over the historical record. However, hotter and colder paleoclimates simulated by CESM2 are inconsistent with paleoclimate evidence. A modified version of CESM2, PaleoCalibr CESM2, may be suitable for paleoclimate studies. Simulations past the end of the 21st century with default CESM2 and studies of microphysical processes in all GCMs should be analyzed with care.

Climate feedbacks are expected to increase over time due to the “pattern effect,” which describes the influence of the pattern of surface warming on global climate feedbacks. Delayed warming in the tropical east Pacific - as seen in abrupt4xCO ₂ simulations - causes increases in climate feedbacks and warming. Several explanations for the relationship between the pattern of sea-surface temperature (SST) warming and climate feedbacks have been proposed, but previous literature has not reached a consensus. Previous literature has often highlighted the role of “tropospheric stability,” whose changes are highly correlated with changes in low clouds with warming. Relatedly, a “circus tent" model posits that in regions of deep convection, convection communicates local SST anomalies vertically. The pattern of SST response also influences the strength of the “Walker circulation”. We introduce a new ensemble of patch experiments and use it, along with observations and reanalysis of the historical period, to evaluate these three proposed mechanisms, in isolation and their relationship to one another. The ensemble of patch experiments is created using the Community Atmosphere Model, version 6 (CAM6) by applying SST patches consistent with the Green's Function Model Intercomparison Project (GFMIP) protocol. This ensemble uses a satellite simulator package, enabling enhanced cloud diagnostics, which reveals that tropical marine low-cloud feedbacks are sensitive to SST anomalies in deep convective regions (nonlocally). Consistently, we find evidence that the circus tent and lower tropospheric stability mechanisms largely explain the relationship between the pattern of SST warming and climate feedbacks and are closely related to one another.

Changes in cloud scattering properties and emissivity that arise from atmospheric warming cause substantial radiative feedbacks in model projections of anthropogenic climate change, and the relative importance of the underlying mechanisms is poorly understood. One leading hypothesis is that ice-to-liquid conversions cause clouds to optically thicken, producing a major negative feedback. We test this hypothesis by developing a method to decompose cloud radiative feedbacks by cloud-top phase. The method is applied to an ensemble of six state-of-the-art global climate models run with prescribed sea surface temperature. In these simulations, the global mean of the net cloud scattering and emissivity feedback from cloud-phase conversions ranges from −0.17 to −0.01 W m⁻² K⁻¹, while the overall net cloud feedback ranges from 0.02 to 0.91 W m⁻² K⁻¹. The multimodel mean of the cloud scattering and emissivity feedback from cloud-phase conversions is approximately 19% of the magnitude of the multimodel mean of the overall cloud feedback (−0.10 vs 0.52 W m⁻² K⁻¹). These results indicate that cloud-phase conversions cause a robust negative feedback by changing cloud scattering and emissivity, but this mechanism makes a modest contribution to the overall cloud feedback at the global scale. Significance Statement: Climate warming changes Earth’s cloud properties, which then change the temperature further. One cloud-climate feedback mechanism involves the conversion of cloud ice particles to liquid droplets with warming. This makes clouds more opaque, causing them to reflect more solar radiation back to space. It is widely accepted that this mechanism dampens climate warming, but its importance relative to other feedback mechanisms has been unclear. This study develops a method to estimate the cloud opacity feedback from ice-to-liquid conversions in climate-model simulations. On average, cloud-opacity changes from phase conversions explain approximately 19% of the overall cloud–climate feedback in an ensemble of six climate models. This finding clarifies the importance of cloud-phase conversions in projections of anthropogenic climate change.

Aerosol–cloud interactions (ACIs) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty in estimating the ERF from aerosol–cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code. Most commonly used methods also cannot disentangle the contributions from different processes to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly averaged cloud fraction partitioned by cloud droplet effective radius (r_e) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed −0.34 ± 0.23, −0.22 ± 0.13, and −0.09 ± 0.11 W m⁻², respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS r_e–LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.

Many geoscience departments in the United States (US) are working to recruit faculty from underrepresented groups. However, there is little information about how hiring practices are perceived by candidates. Here we address this gap by interviewing 19 geoscientists who identify as an underrepresented race, ethnicity, or gender who recently declined a tenure-track faculty job offer in the US about their faculty job searches, with an emphasis on their decisions to accept or decline an offer. We find that many participants experienced hiring practices inconsistent with existing recommendations to increase faculty diversity, and some participants were subject to uncivilized, even potentially discriminatory, practices. Therefore, we leverage our results to provide actionable recommendations for improving faculty recruitment efforts. We highlight that departments may doubly benefit from improving their culture: in addition to benefiting current members, it may also help with recruitment. Overall, our findings emphasize the need for continued evaluation of faculty hiring practices.

The Community Earth System Model version 2 (CESM2) has a higher equilibrium climate sensitivity (ECS) than previous versions of CESM and many other Coupled Model Intercomparison Project (CMIP) models. Relatedly, CESM2 simulates too-cold ice-age and too-hot warm paleoclimates. An inappropriate ice number limiter in the CESM2 microphysics scheme was discovered, and some simulations indicate that the high ECS may be partially attributable to this inappropriate limiter. In light of those findings, we seek to provide users of CESM2 guidance on the fitness of CESM2 for a variety of applications. We find that despite concerns about its climate sensitivity and simulations of past climates, the transient climate response (TCR) in CESM2 is moderate relative to the CMIP6 ensemble and robust across different versions of CESM. The changes made between CESM1 and CESM2 and the fixes to the microphysical issues of CESM2 have little impact on its simulated 20th and 21st century climates under SSP3-7.0. As a result, the simulated 20th and 21st century climates of CESM2 fall well within the range of the CMIP6 ensemble and agree well with observations over the historical record. However, hotter and colder paleoclimates simulated by CESM2 are inconsistent with paleoclimate evidence. A modified version of CESM2, PaleoCalibr CESM2, may be suitable for paleoclimate studies. Simulations past the end of the 21st century with default CESM2 and studies of microphysical processes in all GCMs should be analyzed with care.

Aerosol-cloud interactions (ACI) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty of estimating the ERF from aerosol-cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code and cannot disentangle the contributions from different process to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly-averaged cloud fraction partitioned by cloud droplet effective radius (r_e) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud-fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud, and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed −0.34 ± 0.23, −0.22 ± 0.13, and −0.09 ± 0.11 Wm⁻², respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS r_e– LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.

Aerosol-cloud interactions (ACI) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty of estimating the ERF from aerosol-cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code and cannot disentangle the contributions from different process to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly-averaged cloud fraction partitioned by cloud droplet effective radius (r_e) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud-fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud, and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed −0.34 ± 0.23, −0.22 ± 0.13, and −0.09 ± 0.11 Wm⁻², respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS r_e– LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.

Aerosol-cloud interactions (ACI) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty of estimating the ERF from aerosol-cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code and cannot disentangle the contributions from different process to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly-averaged cloud fraction partitioned by cloud droplet effective radius (r_e) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud-fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud, and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed −0.34 ± 0.23, −0.22 ± 0.13, and −0.09 ± 0.11 Wm⁻², respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS r_e– LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.

Many geoscience departments are taking steps to recruit and retain faculty from underrepresented groups. Here we interview 19 geoscientists who identify as an underrepresented race or gender who recently declined a tenure-track faculty job offer. A range of key factors influenced their decisions to accept or decline a position including commitment to diversity, equity, and inclusion (DEI) including personal identities, DEI initiatives, and mentorship; (in)civility during job interviews; values revealed in negotiation; and compatibility with personal life including family and geography. Many of the participants experienced hiring processes inconsistent with existing recommendations to increase faculty diversity. Therefore, we leverage our results to provide actionable recommendations for improving the equity and effectiveness of faculty recruitment efforts. We find that departments may doubly benefit from improving their culture: in addition to benefiting current members of the department, it may also help with recruitment.