Millennium-length precipitation reconstruction over south-eastern Asia: a pseudo-proxy approach

Abstract

Abstract. Quantifying precipitation variability beyond the instrumental period is essential for putting current and future fluctuations into long-term perspective and providing a test bed for evaluating climate simulations. For south-eastern Asia such quantifications are scarce and millennium-long attempts are still missing. In this study we take a pseudo-proxy approach to evaluate the potential for generating summer precipitation reconstructions over south-eastern Asia during the past millennium. The ability of a series of novel Bayesian approaches to generate reconstructions at either annual or decadal resolutions and under diverse scenarios of pseudo-proxy records' noise is analysed and compared to the classic analogue method. We find that for all the algorithms and resolutions a high density of pseudo-proxy information is a necessary but not sufficient condition for a successful reconstruction. Among the selected algorithms, the Bayesian techniques perform generally better than the analogue method, the difference in abilities being highest over the semi-arid areas and in the decadal-resolution framework. The superiority of the Bayesian schemes indicates that directly modelling the space and time precipitation field variability is more appropriate than just relying on a pool of observational-based analogues in which certain precipitation regimes might be absent. Using a pseudo-proxy network with locations and noise levels similar to the ones found in the real world, we conclude that performing a millennium-long precipitation reconstruction over south-eastern Asia is feasible as the Bayesian schemes provide skilful results over most of the target area.

Technical Note: The analogue method for millennial-scale, spatiotemporal climate reconstructions

“…The choice of a valid analogue usually relies on a distance metric. This is commonly the Euclidean distance (compare Franke et al, 2010;Gómez-Navarro et al, 2017;Talento et al, 2019), although Jensen et al (2018) use an unweighted root 15 mean square error (RMSE) as distance metric between their proxies and the analogue candidates from their simulation pool.…”

“…This drawback also affects the analogue method as shown by Franke et al (2010) and Gómez-Navarro et al (2015), who find that the skill accumulates at the predictor locations of the proxy records. Similarly, Talento et al (2019) highlight that the analogue method may perform badly in regions with little proxy coverage.…”

“…Most paleoclimate applications of the analogue method focussed on the Common Era of the last 2,000 years (e.g., Franke 20 et al, 2010;Trouet et al, 2009;Gómez-Navarro et al, 2015Talento et al, 2019;Neukom et al, 2019). In this context, Graham et al (2007) call the results of the analog method a "proxy surrogate reconstruction".…”

“…The pool of candidate fields depends on the available simulations and reanalyses.If, for example, one uses proxies for temperature, such a ranking may simply provide the model output temperature field that has the smallest Euclidean distance to the sparse proxy information at their locations. Alternatively, one can consider not just one but a small number of good fits with small distances (Franke et al, 2010;Gómez-Navarro et al, 2015Talento et al, 25 2019). However, it is also possible to define a range of tolerable deviations from the proxy predictor values and consider all candidates within this range (compare Bothe and Zorita, 2019).…”

Proxy surrogate reconstructions for Europe and the estimation of their uncertainties

“…It can provide an initial dynamic understanding of past climate variability. However, it is less sophisticated than full data assimilation procedures (compare, e.g., Tardif et al, 2019, anddiscussions in Gómez-Navarro et al, 2017). Graham et al (2007) call reconstructions by analogue "proxy surrogate reconstructions" in an early paleoclimatological application.…”