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We train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics‐based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1,200 ppm CO2 global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network. Integrations with all three data‐driven emulators successfully capture the Quasi‐Biennial Oscillation (QBO) and sudden stratospheric warmings, key modes of stratospheric variability, with the boosted forest more accurate than the random forest in replicating their statistics across our range of carbon dioxide perturbations. The boosted forest and neural network capture the sign of the QBO period response to increased CO2, though both struggle with the magnitude of this response under the more extreme 1,200 ppm scenario. To investigate the connection between performance in the control climate and the ability to generalize, we use techniques from interpretable machine learning to understand how the data‐driven methods use physical information. We leverage this understanding to develop a retraining procedure that improves the coupled performance of the boosted forest in the control climate and under the 800 ppm CO2 scenario. Plain Language Summary: Parameterizations are reduced‐complexity models that estimate the effects of physical processes smaller than what can be resolved by the grid of a weather or climate model. While necessary for realistic simulations, they are a source of uncertainty in climate projections. Recently, machine learning has been used to augment or replace conventional parameterizations of atmospheric gravity waves, a type of motion by which disturbances near the Earth's surface can affect the wind higher up. We compare several machine learning approaches to the gravity wave parameterization problem. In particular, we test neural networks against random and boosted forests, which are built around flowchart‐like models called regression trees. We find that boosted forests, though not widely used for climate model parameterization, are especially successful, scoring as well as or better than neural networks on various performance metrics. We then provide proof‐of‐concept of a novel method to retrain the boosted forest so that it uses its input data more in line with the physics of the system, and show that this technique improves the forest's behavior when used together with an atmospheric model. Key Points: Two kinds of regression forest emulate a gravity wave parameterization offline and online, with boosted forests outperforming random forestsRelative to a neural network benchmark, the boosted forest exhibits similar online skill and ability to generalize to new climatesFeature importance analysis informs a retraining procedure to improve online behavior of data‐driven parameterizations [ABSTRACT FROM AUTHOR]

Historically interorganizational solutions have been sought to some of the more complex problems facing society. Yet, the literature surrounding these types of organizational forms is generally not well developed. In particular, leadership in regards to interorganizational systems is often mentioned but rarely studied. A developing literature concerning 'collaborative leadership' exists but one dealing with leadership in the collaborative interorganizational domain is not as well developed. Collaborative leadership may well encompass what makes a successful interorganizational collaborative leader. On the other hand, it is not without possibility that such abilities do not translate directly to the interorganizational realm. Keywords: interorganizational, leadership, collaboration, interorganizational leadership, participative systems

Spin waves are an emerging alternative to electrical signals for high-speed computing. One approach to realizing spin-wave computing devices is to make use of the diffraction and interference of spin waves in a magnetic thin film. This new class of device requires spin wave transducers that operate across a relatively wide bandwidth. Coplanar-waveguide-based transducers would typically be unsuitable for this application due to their comb-like frequency response. Our simulations show that if the coplanar waveguide (CPW) is placed to the side of the edge of a magnetic film, the frequency response is greatly improved at the expense of peak transducer efficiency. We also found that the CPW on top of the film with an etched gap in the film to the side of the waveguide can be a more efficient transducer for shorter decay lengths. [ABSTRACT FROM AUTHOR]