Abstract
Two statistical methods are tested to reconstruct the inter-annual variations of 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) set-up 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 pseudoproxy 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 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. The addition of noise in the pseudo-proxies is important for the evaluation of the methods, as more spatial differences in the reconstruction skill are revealed. More profound differences between methods are obtained when the number of proxy records is smaller than five, making the Principal Component Regression a more appropriate method in this case. Despite the differences, the results show that the marine network of Arctica islandica can be used to skilfully reconstruct the spatial patterns of SSTs at the eastern NA basin.
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