Abstract
Cloud computing is a mature technology that has already shown benefits for a wide range of academic research domains that, in turn, utilize a wide range of application design models. In this paper, we discuss the use of cloud computing as a tool to improve the range of resources available for climate science, presenting the evaluation of two different climate models. Each was customized in a different way to run in public cloud computing environments (hereafter cloud computing) provided by three different public vendors: Amazon, Google and Microsoft. The adaptations and procedures necessary to run the models in these environments are described. The computational performance and cost of each model within this new type of environment are discussed, and an assessment is given in qualitative terms. Finally, we discuss how cloud computing can be used for geoscientific modelling, including issues related to the allocation of resources by funding bodies. We also discuss problems related to computing security, reliability and scientific reproducibility.
Highlights
The continuous and rapid increase in computing power has been a major factor in the progress of numerous scientific disciplines over the last few decades
Increased computing power in the field of climate modelling is leading to more accurate assessments of the impact of climate change [1]
This implies huge challenges from the point of view of both hardware and software, one of the most important being the ever-increasing volumes of data generated by both observation and simulation. This change, and the commensurate cycle of requiring ever-greater computational resources, is one that is happening across most research domains [2], but is extremely prevalent in the grand challenge areas of climate and geoscience
Summary
The continuous and rapid increase in computing power has been a major factor in the progress of numerous scientific disciplines over the last few decades. Increased computing power in the field of climate modelling is leading to more accurate assessments of the impact of climate change [1] This implies huge challenges from the point of view of both hardware and software, one of the most important being the ever-increasing volumes of data generated by both observation and simulation. This change, and the commensurate cycle of requiring ever-greater computational resources, is one that is happening across most research domains [2], but is extremely prevalent in the grand challenge areas of climate and geoscience. Necessary to bring computing and data together by no longer moving data to computing but computing to data
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