Abstract
Abstract. Paleoclimate proxy data span seasonal to millennial timescales, and Earth's climate system has both high- and low-frequency components. Yet it is currently unclear how best to incorporate multiple timescales of proxy data into a single reconstruction framework and to also capture both high- and low-frequency components of reconstructed variables. Here we present a data assimilation approach that can explicitly incorporate proxy data at arbitrary timescales. The principal advantage of using such an approach is that it allows much more proxy data to inform a climate reconstruction, though there can be additional benefits. Through a series of offline data-assimilation-based pseudoproxy experiments, we find that atmosphere–ocean states are most skillfully reconstructed by incorporating proxies across multiple timescales compared to using proxies at short (annual) or long (∼ decadal) timescales alone. Additionally, reconstructions that incorporate long-timescale pseudoproxies improve the low-frequency components of the reconstructions relative to using only high-resolution pseudoproxies. We argue that this is because time averaging high-resolution observations improves their covariance relationship with the slowly varying components of the coupled-climate system, which the data assimilation algorithm can exploit. These results are consistent across the climate models considered, despite the model variables having very different spectral characteristics. Our results also suggest that it may be possible to reconstruct features of the oceanic meridional overturning circulation based on atmospheric surface temperature proxies, though here we find such reconstructions lack spectral power over a broad range of frequencies.
Highlights
This paper presents a data assimilation approach for paleoclimate reconstructions that can explicitly incorporate proxy data on arbitrary timescales
The primary interest in such a reconstruction technique is that it allows for the inclusion of much more proxy data in climate reconstructions
We performed three types of realistic atmosphere–ocean pseudoproxy reconstructions to assess the impact of using observations at multiple timescales: (1) short pseudoproxies only, (2) long (∼ decadal) pseudoproxies only, and (3) both short and long time-averaged pseudoproxies
Summary
They argue that these improvements arise from the fact that time averaging high-frequency observations improves the signal over noise in the covariance relationship between the atmosphere and the slowly varying ocean overturning circulation We test this hypothesis within a paleoclimate context and assesses whether or not atmosphere–ocean state estimates can be improved by including proxies and climate states at multiple timescales. We extend the technique of Dirren and Hakim (2005), Huntley and Hakim (2010), and Steiger et al (2014) by iteratively applying the state-update equations across multiple timescales by leveraging the serial observation processing approach to the Kalman filter (Houtekamer and Mitchell, 2001). The following general algorithm allows one to assimilate any collection of observation or proxy data, including time averages with irregular duration: 1. Construct a prior (“background”) ensemble xb at the highest temporal resolution of interest (e.g., monthly or annual), or a collection of them with one for each time step (e.g., monthly or annual ensembles assigned to particular months or years)
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