Effects of memory biases on variability of temperature reconstructions

Abstract

Quantifying past climate variation and attributing its causes improves our understanding of the natural variability of the climate system. Tree-ring based proxies have provided skilfull and highly resolved reconstructions of temperature and hydroclimate of the last Millennium. However, like all proxies, they are subject to uncertainties, arising from varying data quality, coverage and reconstruction methodology. Previous studies have suggested that biological-based memory processes could cause spectral biases in climate reconstructions. This study determines the effects of such biases on reconstructed temperature variability and the resultant implications for detection and attribution studies. We find that introducing persistent memory, reflecting the spectral properties of tree-ring data, can change the variability of pseudo proxy reconstructions compared to the surrogate climate and resolve model-proxy-discrepancies. This is especially the case for proxies based on ring-width data. Such memory inflates the difference between the Medieval Climate Anomaly and the Little Ice Age, and suppresses and extends the cooling in response to volcanic eruptions. When accounting for memory effects climate model data can reproduce long-term cooling after volcanic eruptions as seen in proxy reconstructions. Results of detection and attribution studies show that signals in reconstructions as well as residual unforced variability are consistent with those in climate models when the model fingerprints reflect autoregressive memory as found in tree-rings.