Abstract
Abstract. This paper uses a method of atmospheric flow analogues to reconstruct an ensemble of atmospheric variables (namely sea-level pressure, surface temperature and wind speed) between 1781 and 1785. The properties of this ensemble are investigated and tested against observations of temperature. The goal of the paper is to assess whether the atmospheric circulation during the Laki volcanic eruption (in 1783) and the subsequent winter were similar to the conditions that prevailed in the winter 2009/2010 and during spring 2010. We find that the 3 months following the Laki eruption in June 1783 barely had analogues in 2010. The cold winter of 1783/1784 yielded circulation analogues in 2009/2010. The reconstructed surface temperature over land bears significant correlations with local observations, although the amplitude of the reconstruction is weaker.
Highlights
Introduction and motivationThe synoptic variability of the atmospheric circulation during the past centuries has been extensively studied from early instrumental records and proxy reconstructions (Cook et al, 1998; Jones et al, 1999; Luterbacher et al, 2002; Jacobeit et al, 2003)
The goal of this paper is to propose an ensemble of meteorological reconstructions around the North Atlantic from a gridded data set of sea-level pressure that was obtained from early instrumental observations
We show that sea-level pressure (SLP) analogues that are optimized for RMS can yield very low spatial correlation values, the distribution is centred above 0.7
Summary
The synoptic variability of the atmospheric circulation during the past centuries has been extensively studied from early instrumental records and proxy reconstructions (Cook et al, 1998; Jones et al, 1999; Luterbacher et al, 2002; Jacobeit et al, 2003). Such studies have attempted to reconstruct one trajectory of the climate system, giving sometimes loose boundary and initial conditions. It must be remembered that the ensemble spread does not represent the full uncertainty of the reconstruction if the input data are marred with biases, as is the case for reanalyses (Krueger et al, 2013)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
