200 years of European temperature variability: insights from and tests of the proxy surrogate reconstruction analog method

Abstract

Spatially resolved climate reconstructions are commonly derived from long instrumental series and proxy data via linear regression based approaches that use the main modes of the climate system. Such reconstructions have been shown to underestimate climate variability and are based upon the assumption that the main modes of climate variability are stationary back in time. Climate models simulate physically consistent climate fields but cannot be taken to represent the real past climate trajectory because of their necessarily simplified scope and chaotic internal variability. Here, we present sensitivity tests of, and a 200-year temperature reconstruction from, the PSR (Proxy Surrogate Reconstruction) method. This method simultaneously capitalizes on the individual strengths of instrumental/proxy data based reconstructions and model simulations by selecting the model states (analogs) that are most similar with proxy/instrumental data available at specific places and specific moments of time. Sensitivity experiments reveal an optimal PSR configuration and indicate that 6,500 simulation years of existing climate models provide a sufficient pool of possible analogs to skillfully reconstruct monthly European temperature fields during the past 200 years. Reconstruction verification based upon only seven instrumental stations indicates potential for extensions back in time using sparse proxy data. Additionally the PSR method allows evaluation of single time series, in this case the homogeneity of instrumental series, by identifying inconsistencies with the reconstructed climate field. We present an updated European temperature reconstruction including newly homogenized instrumental records performed with the computationally efficient PSR method that proves to capture the total variance of the target.

An ensemble-based approach to climate reconstructions

“…6). This finding is in line with other studies (Brönnimann and Luterbacher, 2004;Rutherford et al, 2005;Franke et al, 2010;Griesser et al, 2010).…”

“…First attempts to assimilate climate proxy information into models include the pioneering work of von Storch et al (2000), Hargreaves and Annan (2002), van der Schrier and Barkmeijer (2005), Goosse et al (2006Goosse et al ( , 2010, and Franke et al (2010). The proposed approaches can be roughly separated into three groups: the methods of von Storch et al and van der Schrier and Barkmeijer seek to push a model simulation towards a large-scale target state through nudging (von Storch et al, 2000) or using singular forcing vectors (van der Schrier and Barkmeijer, 2005).…”

“…The proposed approaches can be roughly separated into three groups: the methods of von Storch et al and van der Schrier and Barkmeijer seek to push a model simulation towards a large-scale target state through nudging (von Storch et al, 2000) or using singular forcing vectors (van der Schrier and Barkmeijer, 2005). The methods by Goosse et al (2006) and Franke et al (2010) select optimal matches with the available proxy information among a set of model states and combine these to "pseudo-simulations". Recently, Goosse et al (2010) modified their assimilation method to produce dynamically consistent past climate states, based on simulations with an Earth system model of intermediate complexity.…”

Pseudo-proxy tests of the analogue method to reconstruct spatially resolved global temperature during the Common Era

“…Recently, it has been implemented in a variety of applications in climate research, from hurricane prediction (Sievers et al, 2000;Fraedrich et al, 2003) to downscaling (Zorita and von Storch, 1999) and upscaling Schenk and Zorita (2012) techniques. For the interest of this study, the suitability of this technique to generate CFRs has been recently demonstrated for temperature (Franke et al, 2010) and precipitation (Gómez-Navarro et al, 2014) for Europe. Although the method is explained elsewhere, we briefly outline its key ideas here, following the notation by Gómez-Navarro et al (2014).…”

“…Furthermore, Nicault et al (2008) used a pseudo-proxy approach similar to the one we use through this work to assess the performance of the reconstruction. In this work, we use the AM to produce a CFR reconstruction following an approach similar to Franke et al (2010) and more recently Gómez-Navarro et al (2014). Used in this way, the method uses a data-based approach to represent the spatial co-variability in the climate fields.…”