Reconstruction of a Two-Dimensional Blocking Index During the Last Four Hundred Years Using Gridded Temperature and Precipitation Data

We present a two-dimensional reconstruction of blocking frequency indices in the Atlantic-European region spanning the last 400 years. Our approach is based on a simple field reconstruction scheme similar to the principal component regression method. The particularity of our reconstruction scheme is that we select the blocking predictors using observed and reconstructed surface temperature and precipitation gridded data based on the correlation stability criteria. This approach avoids the problem of non-stationarity between predictand and predictors that commonly affects the quality of climate field reconstructions. First, we reconstruct the blocking field back to 1891 using observed gridded surface temperature and precipitation data. Then, the reconstruction is extended back in time to 1602 using seasonal-resolution paleo-reanalysis temperature and precipitation fields. The reconstruction is validated against various observed blocking frequency fields and climate reconstruction indices. The methodology presented in this study offers an opportunity for extracting paleo-weather signals from seasonal-resolution gridded datasets, which enables an improved understanding of the forcing of low-frequency variability for atmospheric blockings and related extremes.