Quantification and interpretation of the climate variability record (2101.08050v1)

Abstract: The spectral view of variability is a compelling and adaptable tool for understanding variability of the climate. In Mitchell (1976) seminal paper, it was used to express, on one graph with log scales, a very wide range of climate variations from millions of years to days. The spectral approach is particularly useful for suggesting causal links between forcing variability and climate response variability. However, a substantial degree of variability is intrinsic and the Earth system may respond to external forcing in a complex manner. There has been an enormous amount of work on understanding climate variability over the last decades. Hence in this paper, we address the question: Can we (after 40 years) update the Mitchell (1976) diagram and provide it with a better interpretation? By reviewing both the extended observations available for such a diagram and new methodological developments in the study of the interaction between internal and forced variability over a wide range of timescales, we give a positive answer to this question. In addition, we review alternative approaches to the spectral decomposition and pose some challenges for a more detailed quantification of climate variability.

• The paper updates the Mitchell spectrum diagram using new observational data and advanced spectral methods to quantify climate variability.
• It demonstrates that both internal processes, such as ENSO, and external forcings like solar variability jointly shape Earth's climate signals.
• Modern techniques including wavelet analysis and sparse decomposition uncover nonlinear and non-stationary dynamics underpinning climate variability.

Quantification and Interpretation of the Climate Variability Record

"Quantification and Interpretation of the Climate Variability Record" examines advances in understanding climate variability across various temporal and spatial scales, focusing on updating and interpreting the Mitchell (1976) spectrum diagram. The paper integrates recent observational data, methodological innovations, and theoretical developments to illustrate climate variability's complex dynamics, emphasizing spectral decomposition while addressing interaction between internal processes and external forcings.

Climate variability encompasses processes from diurnal cycles to geological timescales, creating a spectrum of variability driven by both internal dynamics and external forcings. The spectral approach remains central to disentangling these components, facilitating the identification of causal links between forces like solar insolation and corresponding climate responses. Despite advancements, challenges persist in quantifying contributions from intrinsic and external variations and understanding their interactions over multiple timescales.

The updating of the Mitchell spectrum is pivotal, as it captures advancements in climate science. By incorporating newer observational data and refined spectral methods, the paper extends the work of Mitchell and provides a more nuanced interpretation of the MS, suggesting new approaches to decompose the climate spectrum and address its multifaceted nature. This updated perspective reveals more comprehensive interactions across different climate systems, offering insights into processes previously unaddressed by the original MS framework.

The paper explores the role of external forcings, such as orbital changes and solar variability, and their imprints on climate signals across eons. These drivers generate distinct spectral characteristics, yet the Earth's climate response is inherently nonlinear, as evidenced by phenomena like the Pleistocene glacial cycles. Such insights stress the complexity underlying apparent periodicities, raising the necessity for precise mathematical approaches to decode these influences effectively.

Internal variability is also scrutinized, highlighted by modes such as the El Niño-Southern Oscillation (ENSO) and the Atlantic Multidecadal Variability (AMV). These processes, influencing climatic conditions on interannual to centennial scales, display interactions between intrinsic and extrinsic factors that are integral to understanding climatic variability. However, distinguishing impactful forcings from internal noise in paleo-records poses a significant challenge, demanding advanced analytical techniques and comprehensive modeling.

Modern advancements in data science, notably the application of wavelet analysis and sparse-decomposition techniques, facilitate the evaluation of evolving climatic signals in non-stationary datasets. These methods allow researchers to extract meaningful insights regarding climate dynamics' nonlinear and non-stationary characteristics, providing granular perspectives on temporal and spatial variability hitherto inaccessible through traditional statistical methods.

The theoretical underpinning of the paper emphasizes the interaction between climate and external forcing, examining synchronization, resonance phenomena, and tipping points within the climate system. The discussion on forced dynamical systems, nonlinear feedbacks, and multiscale dynamics underscores the complexities in predicting climatic responses to anthropogenic impacts and natural variabilities.

In conclusion, this review serves as an advanced synthesis of developments since Mitchell's pivotal work, offering a robust foundation for future explorations into climate variability. It draws attention to the continued need for refined methodologies and theoretical frameworks to grasp the intricacies of Earth’s climate systems. Future research should focus on enhancing model fidelity, especially in capturing nonlinear interactions and feedback processes critical to understanding and predicting climate changes on multiple scales.