Abstract
Three different climate field reconstruction (CFR) methods employed to reconstruct North Atlantic-European (NAE) and Northern Hemisphere (NH) summer season temperature over the past millennium from proxy records are tested in the framework of pseudoproxy experiments derived from three climate simulations with Earth System Models. Two of these methods are traditional multivariate linear methods (Principal Components Regression, PCR and Canonical Correlation Analysis, CCA), whereas the third method (Bidirectional Long-Short-Term Memory Neural Network, Bi-LSTM) belongs to the category of machine learning methods. The Bi-LSTM method does not need to assume linear and temporally stable relationships between the underlying proxy network and the targeted climate field, in contrast to PCR and CCA. In addition, Bi-LSTM incorporates information on the serial correlation of the time series. All three methods tested herein achieve reasonable reconstruction performance in both spatial and temporal scale. Generally, the reconstruction skill is higher in regions with denser proxy coverage, but reconstruction skill is also achieved in proxy-free areas due to climate teleconnections. All three CFR methodologies generally tend to more strongly underestimate the target temperature variations as more noise is introduced into the pseudoproxies. The Bi-LSTM method tested in our experiments shows relatively worse reconstruction skills compared to PCR and CCA, yet it brings some encouraging results on capturing extreme cooling climate signals. This indicates that this nonlinear CFR method could be a potential methodology for past climate extremes analysis.
Highlights
The reconstruction of past climates helps to better understand past climate variability and pose the projected future climate evolution against the backdrop of natural climate variability (PAGES 2k Consortium, 2013, 2017, 2019; PAGES Hydro2k Consortium, 2017; Schmidt, 2010; Evans et al, 2014; Christiansen and Ljungqvist, 2016)
The Bidirectional LongShort-Term Neural Network (Bi-Long Short-Term Memory Network (LSTM)) method tested in our experiments shows relatively worse reconstruction skills compared to Principal Component Regression (PCR) and Canonical Correlation Analysis (CCA), yet it brings some encouraging results on capturing extreme cooling climate signals
This indicates that all three climate field reconstruction (CFR) methods show generally reasonable spatial reconstruction skills
Summary
The reconstruction of past climates helps to better understand past climate variability and pose the projected future climate evolution against the backdrop of natural climate variability (PAGES 2k Consortium, 2013, 2017, 2019; PAGES Hydro2k Consortium, 2017; Schmidt, 2010; Evans et al, 2014; Christiansen and Ljungqvist, 2016). The skill of the statistical method, the impact of proxy network coverage and of the amount of climate signal present in the proxy records can be evaluated in that virtual reality of climate models, once adequate synthetic proxy records are constructed These tests are generally denoted pseudo-proxy experiments (PPEs, Smerdon, 2012, Gómez-Navarro et al, 2017). St. George and Esper concluded that the present-day generation of tree ring proxy based reconstructions exhibit high correlations with seasonal hemispheric summer temperatures and display relatively better skills in tracking year-to-year climatic variabilities and decadal fluctuations than former proxy networks, as found by Wilson et al, (2016) and Anchukaitis 85 et al, (2017). Among the models providing climate simulations of the past millennium, these three models are the ones with the highest horizontal resolution
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