2020
DOI: 10.1007/s00382-020-05444-7
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Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition

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Cited by 7 publications
(4 citation statements)
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“…One advantage of the FISM is that only one parameter (the integral order q, see the "Methods" section) is required to extract the direct-forcingresponse and the indirect-memory-response from x(t), and the parameter can be objectively measured from the climatic variable of interest. Accordingly, the FISM has been successfully applied for various aspects, e.g., estimating climate predictability with climate memory effects properly considered 44 , correcting tree-ring width based paleo-reconstructions with non-climatic persistence reasonably removed 45 , among others. Here in this study, we focus on global mean surface temperature anomalies (GMTA).…”
Section: Introductionmentioning
confidence: 99%
“…One advantage of the FISM is that only one parameter (the integral order q, see the "Methods" section) is required to extract the direct-forcingresponse and the indirect-memory-response from x(t), and the parameter can be objectively measured from the climatic variable of interest. Accordingly, the FISM has been successfully applied for various aspects, e.g., estimating climate predictability with climate memory effects properly considered 44 , correcting tree-ring width based paleo-reconstructions with non-climatic persistence reasonably removed 45 , among others. Here in this study, we focus on global mean surface temperature anomalies (GMTA).…”
Section: Introductionmentioning
confidence: 99%
“…From this perspective, the FISM model has been widely used, including climate predictability studies and model evaluations (Nian et al., 2020; Xiong et al., 2019). In this study, we will employ this model to address the impacts of external forcings across scales.…”
Section: Introductionmentioning
confidence: 99%
“…Note that the source of climate predictability is a broad concept. The inertia or memory of climate variables, the response of climate to external forcings (e.g., solar activity, anthropogenic greenhouse gas emissions) and the interactions among different spheres and processes in climate system are recognized as three types of sources of climate predictability (Nian et al, 2020). This study focuses on the aforementioned third type of source of climate predictability.…”
Section: Introductionmentioning
confidence: 99%