Long-memory effects in linear-response models of Earth's temperature and implications for future global warming
Martin Rypdal, Kristoffer Rypdal

TL;DR
This paper introduces a long-memory energy-balance model for Earth's temperature, showing it better explains temperature data and forcing contributions over centuries, with implications for understanding climate sensitivity and future warming.
Contribution
The paper develops a novel long-memory response model for Earth's temperature, providing improved analysis of temperature records and forcing contributions over millennia.
Findings
Residuals are consistent with long-memory processes.
Volcanic aerosols significantly contributed to Little Ice Age cooling.
Model predicts a time-scale dependent climate sensitivity.
Abstract
A linearized energy-balance model for global temperature is formulated, featuring a scale-free long-range memory (LRM) response and stochastic forcing representing the influence on the ocean heat reservoir from atmospheric weather systems. The model is parametrized by an effective response strength, the stochastic forcing strength, and the memory exponent. The instrumental global surface temperature record and the deterministic component of the forcing are used to estimate these parameters by means of the maximum-likelihood method. The residual obtained by subtracting the deterministic solution from the observed record is analyzed as a noise process and shown to be consistent with a long-memory time-series model and inconsistent with a short-memory model. By decomposing the forcing record in contributions from solar, volcanic, and anthropogenic activity one can estimate the contribution…
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