Climate processes

Abstract:

Large computer models are ubiquitous in the Earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core hours to run to completion while generating terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties, and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce ESEm: an open-source tool providing a general workflow for emulating and validating a wide variety of models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well-established, high-performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of ESEm through three case studies using ESEm to reduce parametric uncertainty in a general circulation model and explore precipitation sensitivity in a cloud-resolving model and scenario uncertainty in the CMIP6 multi-model ensemble.

Abstract:

<jats:p>Abstract. Large computer models are ubiquitous in the Earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core hours to run to completion while generating terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties, and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce ESEm: an open-source tool providing a general workflow for emulating and validating a wide variety of models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well-established, high-performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of ESEm through three case studies using ESEm to reduce parametric uncertainty in a general circulation model and explore precipitation sensitivity in a cloud-resolving model and scenario uncertainty in the CMIP6 multi-model ensemble. </jats:p>

Abstract:

In this study, we examine the single-scattering properties of aggregates of nano-sized spherical scattering centers (spherules) of different sizes and refractive indices, with an emphasis on contrasting the single-scattering properties of ordered and disordered aggregates, holding all other parameters constant. The ordered aggregates are constructed by arranging the spherules in a simple cubic configuration, while the disordered aggregates are constructed using an ideal amorphous solid algorithm. The single-scattering properties of both kinds of aggregates are computed using the superposition T-matrix method. We find that, in most cases, the scattering and absorption and hence extinction of radiation by ordered aggregates is stronger than for disordered aggregates: for the cases we examine, the average percent difference in scattering efficiency is 14%, and the maximum percent difference in scattering efficiency is 44%; the average percent difference in absorption efficiency is 5.3%, and the maximum percent difference in absorption efficiency is 18%; the average percent difference in extinction efficiency is 12%, and the maximum percent difference in extinction efficiency is 40%. These differences have implications regarding radiative transfer in systems of amorphous particles in general.