Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition

Nian, Da; Yuan, Naiming; Ying, Kairan; Liu, Ge; Fu, Zuntao; Qi, Yanjun; Franzke, Christian

doi:10.1007/s00382-020-05444-7

Cited by 7 publications

(4 citation statements)

References 48 publications

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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%

The impact of long-term memory on the climate response to greenhouse gas emissions

et al. 2022

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Global warming exerts a strong impact on the Earth system. Despite recent progress, Earth System Models still project a large range of possible warming levels. Here we employ a generalized stochastic climate model to derive a response operator which computes the global mean surface temperature given specific forcing scenarios to quantify the impact of past emissions on current warming. This approach enables us to systematically separate between the “forcing-induced direct” and the “memory-induced indirect” trends. Based on historical records, we find that the direct-forcing-response is weak, while we attribute the major portion of the observed global warming trend to the indirect-memory responses that are accumulated from past emissions. Compared to CMIP6 simulations, our data-driven approach projects lower global warming levels over the next few decades. Our results suggest that CMIP6 models may have a higher transient climate sensitivity than warranted from the observational record, due to them having larger long-term memory than observed.

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Section: Introductionmentioning

confidence: 99%

The impact of long-term memory on the climate response to greenhouse gas emissions

et al. 2022

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“…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%

Separating Anthropogenically‐ and Naturally‐Caused Temperature Trends: A Systematic Approach Based On Climate Memory Analysis

Yuan

Franzke

et al. 2023

Geophysical Research Letters

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It has been well recognized that the observed temperature changes consist of effects from anthropogenic forcings (e.g., greenhouse gases, land-use change), natural forcings (e.g., volcanic eruptions, solar activity), and internal natural variabilities (Eyring et al., 2021), but separating their contributions is challenging. Straightforwardly, one may achieve this by comparing observations with model simulations under different forcings, but this is limited by the ability of current coupled models in capturing the internal natural variability of the climate system (Fyfe et al., 2013). For example, many models missed the simulation of the recent global warming hiatus (GWH) from 1998 to 2012 (Papalexiou et al., 2020). In recent years, great efforts have been made in Detection and Attribution studies, and a sophisticated way is to carry out generalized linear regressions of model simulations against observational data, that is, the optimal fingerprinting (OFP) method (Allen & Tett, 1999;Hasselmann, 1997). The OFP method is able to distinguish the contributions of different factors on observed climate changes, but model simulations usually require massive computing resources and have uncertainties particularly on regional scales (Bindoff et al., 2013). To meet the demands of Detection and Attribution science, statistical approaches based solely on

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“…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%

Sources of the predictability of month‐to‐month variation of precipitation anomalies in East Asia during summer

Sun,

Li,

Zhu

et al. 2023

Intl Journal of Climatology

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During boreal summer, the patterns of precipitation anomalies in East Asia have notable month‐to‐month variations, which may induce alternated wet and dry conditions in different regions of East Asia during summer. In this study, based on observation and reanalysis data, the dynamic processes of monthly precipitation anomalies in three sub‐regions of East Asia (South China, the Yangtze River valley and northern–northeastern China) are investigated, which are associated with different combined effects of tropical air–sea interaction and mid‐to‐high latitude Rossby waves during different months. These dynamic processes dominate the leading modes of month‐to‐month variation of precipitation anomalies in East Asia during summer. To understand what dynamic processes are the key sources of the predictability of month‐to‐month variation of precipitation anomalies in East Asia during summer, a method of spatially running pattern correlation coefficient (SR_PCC) is applied to examine the differences in prediction skill of geopotential height anomalies, sea‐level pressure anomalies and sea surface temperature anomalies between the years of well and poorly predicted precipitation anomalies. Based on the results of CFSv2 hindcast and CMIP6‐AMIP experiments, both the air–sea interaction over tropical Indo–Pacific region and the mid‐to‐high latitude Rossby waves over the Eurasian continent are key sources of predictability of month‐to‐month variation of precipitation anomalies in East Asia during boreal summer.

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Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition

Cited by 7 publications

References 48 publications

The impact of long-term memory on the climate response to greenhouse gas emissions

The impact of long-term memory on the climate response to greenhouse gas emissions

Separating Anthropogenically‐ and Naturally‐Caused Temperature Trends: A Systematic Approach Based On Climate Memory Analysis

Sources of the predictability of month‐to‐month variation of precipitation anomalies in East Asia during summer

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