Pseudo-proxy evaluation of climate field reconstruction methods of North Atlantic climate based on an annually resolved marine proxy network

Abstract

Abstract. Two statistical methods are tested to reconstruct the interannual variations in past sea surface temperatures (SSTs) of the North Atlantic (NA) Ocean over the past millennium based on annually resolved and absolutely dated marine proxy records of the bivalve mollusk Arctica islandica. The methods are tested in a pseudo-proxy experiment (PPE) setup using state-of-the-art climate models (CMIP5 Earth system models) and reanalysis data from the COBE2 SST data set. The methods were applied in the virtual reality provided by global climate simulations and reanalysis data to reconstruct the past NA SSTs using pseudo-proxy records that mimic the statistical characteristics and network of Arctica islandica. The multivariate linear regression methods evaluated here are principal component regression and canonical correlation analysis. Differences in the skill of the climate field reconstruction (CFR) are assessed according to different calibration periods and different proxy locations within the NA basin. The choice of the climate model used as a surrogate reality in the PPE has a more profound effect on the CFR skill than the calibration period and the statistical reconstruction method. The differences between the two methods are clearer for the MPI-ESM model due to its higher spatial resolution in the NA basin. The pseudo-proxy results of the CCSM4 model are closer to the pseudo-proxy results based on the reanalysis data set COBE2. Conducting PPEs using noise-contaminated pseudo-proxies instead of noisefree pseudo-proxies is important for the evaluation of the methods, as more spatial differences in the reconstruction skill are revealed. Both methods are appropriate for the reconstruction of the temporal evolution of the NA SSTs, even though they lead to a great loss of variance away from the proxy sites. Under reasonable assumptions about the characteristics of the non-climate noise in the proxy records, our results show that the marine network of Arctica islandica can be used to skillfully reconstruct the spatial patterns of SSTs at the eastern NA basin.

Calibration Uncertainties of Tropical Pacific Climate Reconstructions over the Last Millennium

“…The filled circles and error bars indicate the mean and interensemble STD for the 10 members. In agreement with previous studies (e.g., Christiansen et al 2009;Pyrina et al 2017;von Storch et al 2004), we show an underestimation of the reconstructed GMT variance relative to the original simulation. There is also a large spread of the amplitude of reconstructed GMT variabilities for different calibration periods.…”

“…Moreover, ENSO's potential response to external drivers has not been well understood (e.g., Hope et al 2017;Wilson et al 2010). Despite several efforts to test this nonstationarity issue (Christiansen 2011;Christiansen et al 2009;Esper et al 2005;Pyrina et al 2017;Rutherford et al 2003), it still remains unclear whether the choice of a twentieth-century calibration period will affect regional-scale CFRs of internally generated and externally forced LM temperature variability in key areas such as the tropical Pacific.…”

The revolution of crossdating in marine palaeoecology and palaeoclimatology

“…One such approach is to use these records to compare with, calibrate, test, benchmark or assimilate into general circulation models (GCMs) [48]. Sclerochronological records can also be used to assess longer-term bias, quantify the amplitude and spatial patterns of uncertainties in GCM runs compared to instrumental data products, and to evaluate climate field reconstruction methods [49]. The quantification and characterization of these uncertainties coupled with the general improvement in our understanding of the forcing mechanisms that drive the coupled ocean -atmosphere climate system will ultimately facilitate the continued improvement of the individual GCMs, enhancing the ability of the numerical models to provide robust simulations of likely future climate change.…”

“…The quantification and characterization of these uncertainties coupled with the general improvement in our understanding of the forcing mechanisms that drive the coupled ocean -atmosphere climate system will ultimately facilitate the continued improvement of the individual GCMs, enhancing the ability of the numerical models to provide robust simulations of likely future climate change. Numerical models can also be used to identify and guide selection of sites where new chronologies likely have maximum palaeoclimatic significance [49,50]. Finally, crossdated marine chronologies can constrain quasi/multi-decadal climate variability over the past few centuries to millennia [9].…”

Evaluation of statistical climate reconstruction methods based on pseudoproxy experiments using linear and machine learning methods

“…Traditional CFRs usually assume linear and temporally stable relationships between the local variables captured by the proxy network and the target climate field. Likewise, the spatial patterns of climate variability are considered as stationary (Pyrina et al, 2017;Wang et al, 2014;Smerdon et al, 2016). However, climate change is dynamic and chaotic, and many links between climate fields can be non-linear (Schneider et al, 2018;Dueben and Bauer, 2018;Huntingford et al, 2019;Nadiga, 2020).…”