Monthly mean pressure reconstruction for the Late Maunder Minimum Period (AD 1675–1715)

• Main Authors: Luterbacher, J., Rickli, R., Tinguely, C., Xoplaki, E., Schüpbach, E., Dietrich, D., Hüsler, J., Ambühl, M., Pfister, C., Beeli, P., Dietrich, U., Dannecker, A., Davies, T.D., Jones, P.D., Slonosky, V., Ogilvie, A.E.J., Maheras, P., Kolyva‐Machera, F., Martin‐Vide, J., Barriendos, M., Alcoforado, Maria Joao, Nunes, M.F., Jónsson, T. J, Glaser, R., Jacobeit, J., Beck, C., Philipp, A., Beyer, U., Kaas, E., Schmith, T., Bärring, L., Jönsson, P., Rácz, L., Wanner, H.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Royal Meteorological Society 2019
• Subjects: Canonical correlation analysis; Europe; Maunder Minimum; Proxy data; Sea level pressure reconstruction; Øresund; Iceland; North Atlantic; Sea ice
• Online Access: http://hdl.handle.net/10451/37082

The Late Maunder Minimum (LMM; 1675–1715) delineates a period with marked climate variability within the Little Ice Age in Europe. Gridded monthly mean surface pressure fields were reconstructed for this period for the eastern North Atlantic–European region (25°W–30°E and 35–70°N). These were based on continuous information drawn from proxy and instrumental data taken from several European data sites. The data include indexed temperature and rainfall values, sea ice conditions from northern Iceland and the Western Baltic. In addition, limited instrumental data, such as air temperature from central England (CET) and Paris, reduced mean sea level pressure (SLP) at Paris, and monthly mean wind direction in the Øresund (Denmark) are used. The reconstructions are based on a canonical correlation analysis (CCA), with the standardized station data as predictors and the SLP pressure fields as predictand. The CCA-based model was performed using data from the twentieth century. The period 1901–1960 was used to calibrate the statistical model, and the remaining 30 years (1961–1990) for the validation of the reconstructed monthly pressure fields. Assuming stationarity of the statistical relationships, the calibrated CCA model was then used to predict the monthly LMM SLP fields. The verification results illustrated that the regression equations developed for the majority of grid points contain good predictive skill. Nevertheless, there are seasonal and geographical limitations for which valid spatial SLP patterns can be reconstructed. Backward elimination techniques indicated that Paris station air pressure and temperature, CET, and the wind direction in the Øresund are the most important predictors, together sharing more than 65% of the total variance. The reconstructions are compared with additional data and subjectively reconstructed monthly pressure charts for the years 1675–1704. It is shown that there are differences between the two approaches. However, for extreme years the reconstructions are in good agreement. ...