Graduate Thesis Or Dissertation
 

Investigating climate feedbacks across forcing magnitudes and time scales using the radiative kernel technique

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/02871001c

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  • Radiative feedbacks associated with changes in water vapor, temperature, surface albedo and clouds remain a major source of uncertainty in our understanding of climate's response to anthropogenic forcing. In this dissertation climate model data is used to investigate variations in feedbacks that result from changing CO₂ forcing and the time scales on which feedbacks operate, focusing on the applicability of one method in particular, the radiative kernel technique, to these problems. This computationally efficient technique uses a uniform, incremental change in feedback variables to infer top-of-atmosphere (TOA) radiative flux changes. The first chapters explore the suitability of the linear radiative kernel technique for large forcing scenarios. We show that kernels based on the present-day climate misestimate TOA flux changes for large perturbations, translating into biased feedback estimates. We address this issue by calculating additional kernels based on a large forcing climate state with eight times present day CO₂ concentrations. Differences between these and the present-day kernels result from added absorption of radiation by CO₂ and water vapor, and increased longwave emission due to higher temperatures. Combining present-day and 8xCO₂ kernels leads to significant improvement in the approximation of TOA flux changes and accuracy of feedback estimates. While climate sensitivity remains constant with increasing CO₂ forcing when the inaccurate present-day kernels are used, sensitivity increases significantly when new kernels are used. Comparison of feedbacks in climate models with observations is one way towards understanding the disagreement among models. However, climate change feedbacks operate on time scales that are too long to be evaluated from the observational record. Rather, short-term proxies for greenhouse-gas-driven warming are often used to compute feedbacks from observations. The third chapter of this dissertation examines links between the seasonal cycle and global warming using pattern correlations of spatial distribution of feedback variables and radiative flux changes. We find strong correlations between time scales for changes in surface temperature and climate variables, but not for TOA flux anomalies, reaffirming conclusions drawn in previous work. Finally, we investigate the fitness of the radiative kernel technique for evaluation of short-term feedbacks in a comparison with the more accurate, but more computationally expensive, partial radiative perturbations.
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