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Interdisciplinary education offers a collaborative approach to multifaceted topics, such as environmental sustainability. In this article, we present the conceptual framework and lessons learned from a teamtaught course, entitled Sustainability, Energy, and the Green Economy (SEGE). Faculty from the departments of Physics, Chemistry, and Biology offered the course during the 2015–2016 and 2016–2017 academic years at Bronx Community College (BCC). The described interdisciplinary approach was intended to engage students through research-driven, project-based learning using life cycle analysis (LCA) of a simple consumer product. By teaching solutionoriented approaches, SEGE translates complex real-world problems into classroom learning for non-STEM majors. As compared to student performance and engagement data from three 100-level STEM courses at BCC, analyses of C or above grade performance, passing rate, and withdrawal rate indicate that SEGE effectively engaged and retained students. Additionally, student surveys are presented, and were used to guide instructional strategy for effective team teaching and assessment.

The energy balance of Earth is influenced by many factors such as temperature, wind, the amount of water vapor in the air as well as surface albedo and emittance. Radiative transfer is the transfer of electromagnetic radiation in scattering, absorbing and emitting media. Instruments deployed on weather balloons, aircrafts, and satellites are used to collect data to characterize the atmosphere including its scattering and absorption properties, either locally or by remote sensing. Radiative transfer models (RTMs) are developed and used extensively for a wide range of applications in remote sensing and space technology. This paper is intended to provide a coherent summary of RTMs and associated databases publicly available, as well as some patents and publications related directly or indirectly to the field of radiative transfer and the use of RTMs in remote sensing applications. The computer codes described in this paper reflect some of the latest advancements in the analysis of data related to Earth's radiation budget and their use to enhance understanding of climate change and global warming. The applications of radiative transfer occur mainly in areas of climate modeling, atmospheric science, ocean science, remote sensing, astronomy, astrophysics, hydrodynamics, and optics. Radiative transfer codes are used in this broad range of applications, including many related to weather, climate and satellite meteorology. This paper concludes with a description of the future developments expected in the subject covered.

This study aims to create observation‐based cloud radiative kernel (CRK) datasets and evaluate them by direct comparison of CRK and the CRK‐derived cloud feedback datasets. Based on the International Satellite Cloud Climatology Project (ISCCP) H datasets, we calculate CRKs (called ISCCP‐FH or FH CRKs) as 2D joint function/histogram of cloud optical depth and cloud top pressure for shortwave (SW), longwave (LW), and their sum, Net, at the top of atmosphere (TOA), as well as, for the first time, at the surface (SFC) and in the atmosphere (ATM). With cloud fraction change (CFC) datasets from doubled‐CO2simulation and short‐term observational anomalies, we derive all the TOA, SFC and ATM cloud feedback for SW, LW and Net using our CRKs.The direct comparison with modeled and observed CRKs (or cloud radiative effects), cloud feedback from previous model results and the Clouds and the Earth's Radiant Energy System products show that our CRKs and CRK‐derived cloud feedback are reasonably well validated. We estimate the uncertainty for the CRK‐derived cloud feedback and show that the CFC‐associated uncertainty contributes >98.5% of the total cloud feedback uncertainty while CRK's is very small. Our preliminary evaluation also shows that some near‐zero/small cloud feedback in the TOA‐alone feedback indeed results from the compensation of sizable cloud feedback of the SFC and ATM feedback and reveals some significant surface and atmospheric cloud feedback whose sum appears insignificant in TOA‐alone feedback. In addition, the atmospheric longwave cloud feedback seems to play a role in enhancing meridional atmospheric energy transport. The cloud radiative kernel datasets are created for longwave, shortwave and their sum, net, respectively, at the top of atmosphere, the surface and in the atmosphere. They are evaluated by direct comparison with the counterparts of the other three representative datasets and with the cloud radiative effects from the Clouds and the Earth's Radiant Energy System products. In addition, the kernel‐derived cloud feedback is also directly compared with the previous ensemble results from 10 climate models and the observation‐based results from the Clouds and the Earth's Radiant Energy System products. Both comparisons well‐validated our cloud kernels and their derived cloud feedbackThe uncertainty budget for cloud kernel‐derived cloud feedback at the top of atmosphere is estimated based on uncertainties of cloud kernels and cloud fraction change from the direct comparisons. It shows that the cloud fraction change associated uncertainty contributes >98.5% of the total cloud feedback uncertainty while cloud kernels' is very small. We have also tested the effect of dry bias of water vapor on the cloud radiative kernel calculation when the clear‐sky water vapor is used for the cloud‐free layers under cloud layers and estimated error/uncertainty may be caused in the calculation by such a dry bias. The results show such‐caused errors/uncertainties are in high order and need not to be taken into account at presentOur preliminary evaluation also shows that some near‐zero or small cloud feedback in the top‐of‐atmosphere‐alone feedback results from the compensation of sizable cloud feedback of the surface and atmospheric feedback. It demonstrates how the surface and atmospheric cloud kernel‐derived cloud feedback can be used to reveal some significant surface and atmospheric cloud feedback whose sum appears insignificant in the top‐of‐atmosphere‐alone feedback. In addition, the atmospheric longwave cloud feedback may play a role in enhancing meridional atmospheric energy transport The cloud radiative kernel datasets are created for longwave, shortwave and their sum, net, respectively, at the top of atmosphere, the surface and in the atmosphere. They are evaluated by direct comparison with the counterparts of the other three representative datasets and with the cloud radiative effects from the Clouds and the Earth's Radiant Energy System products. In addition, the kernel‐derived cloud feedback is also directly compared with the previous ensemble results from 10 climate models and the observation‐based results from the Clouds and the Earth's Radiant Energy System products. Both comparisons well‐validated our cloud kernels and their derived cloud feedback The uncertainty budget for cloud kernel‐derived cloud feedback at the top of atmosphere is estimated based on uncertainties of cloud kernels and cloud fraction change from the direct comparisons. It shows that the cloud fraction change associated uncertainty contributes >98.5% of the total cloud feedback uncertainty while cloud kernels' is very small. We have also tested the effect of dry bias of water vapor on the cloud radiative kernel calculation when the clear‐sky water vapor is used for the cloud‐free layers under cloud layers and estimated error/uncertainty may be caused in the calculation by such a dry bias. The results show such‐caused errors/uncertainties are in high order and need not to be taken into account at present Our preliminary evaluation also shows that some near‐zero or small cloud feedback in the top‐of‐atmosphere‐alone feedback results from the compensation of sizable cloud feedback of the surface and atmospheric feedback. It demonstrates how the surface and atmospheric cloud kernel‐derived cloud feedback can be used to reveal some significant surface and atmospheric cloud feedback whose sum appears insignificant in the top‐of‐atmosphere‐alone feedback. In addition, the atmospheric longwave cloud feedback may play a role in enhancing meridional atmospheric energy transport