Abstract
Abstract. We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state. We use this technique to determine the steady-state responses of low cloud fraction and latent heat flux to heating perturbations over 22 regions spanning Earth's oceans. We show that the response characteristics are similar to those of step-change simulations, but in this new method the responses for 22 regions can be characterized simultaneously. Furthermore, we can estimate the timescale over which the steady-state response emerges. The proposed methodology could be useful for a wide variety of purposes in climate science, including characterization of teleconnections and uncertainty quantification to identify the effects of climate model tuning parameters.
Highlights
Understanding the response of climate models to perturbations is one of the core questions in climate science
Some of the emergent behaviors in climate model response, on small temporal and spatial scales, can be challenging to interpret. This is in part due to issues with low signal-tonoise ratios (SNRs), climate system nonlinearities, and other far-field effects
As we discuss many types of simulations that are commonly employed in climate science to investigate climate model response suffer from issues associated with this tradeoff
Summary
Understanding the response of climate models to perturbations is one of the core questions in climate science. Some of the emergent behaviors in climate model response, on small temporal and spatial scales, can be challenging to interpret This is in part due to issues with low signal-tonoise ratios (SNRs), climate system nonlinearities, and other far-field effects. Any input signal will result in a portion of the response that is linear and a portion that is nonlinear, and increasing the magnitude of the input has the potential to amplify nonlinearities Avoiding this prospect requires multiple ensemble members or longer simulations to increase SNR, which becomes quite expensive if one wishes to assess multiple perturbations (e.g., changes in multiple geographical regions). Many types of simulations that are commonly employed in climate science to investigate climate model response suffer from issues associated with this tradeoff They are not designed to investigate multiple input–output relationships simultaneously, necessitating larger computational cost to investigate complex systems. Kravitz et al.: System identification the process of system identification, its utility as compared to other commonly used methods of assessing climate system behavior, and potential implications for understanding far-field effects
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