Seasonal reconstructions coupling ice core data and an isotope enabled climate model – implications of seasonality, climate modes and selection of proxy data

• Main Authors: Sjolte, Jesper, Adolphi, Florian, M. Vinther, Bo, Muscheler, Raimund, Sturm, Christophe, Werner, Martin, Lohmann, Gerrit
• Format: Text
• Language: English
• Published: 2019
• Subjects: Greenland; Greenland ice cores; ice core; North Atlantic
• Online Access: https://doi.org/10.5194/cp-2019-136; https://cp.copernicus.org/preprints/cp-2019-136/

The research area of climate field reconstructions has developed strongly during the past 20 years, motivated by the need to understand the complex dynamics of the earth system in a changing climate. Climate field reconstructions aim to build a consistent gridded climate reconstruction of different variables, often from a range of climate proxies, using either statistical tools or a climate model to fill the gaps between the locations of the proxy data. In most cases large scale climate field reconstructions covering more than 500 years are of annual resolution. Here we investigate the potential of seasonally resolved climate field reconstructions based on oxygen isotope records from Greenland ice cores and an isotope enabled climate model. We test a range of climate reconstructs varying the definition of the seasons and the number of ice cores used. Our findings show that the optimal definition of the seasons depends on the variability of the target season. For winter, the vigorous variability is best captured when defining the season December–February due to the dominance of large scale patterns, while for summer the weaker, albeit more strongly auto-correlated, variability is better captured using a longer season of May–Oct. Motivated by the scarcity of seasonal data we also test the use of annual data where the year is divided during summer, that is, not following the calendar year. This means that the winter variability is not split, and that the annual data then can be used to reconstruct the winter variability. In particularly when reconstructing the sea level pressure, and the corresponding main modes of variability, it is important to take seasonality into account, because of changes in the spatial patterns of the modes throughout the year. Targeting the annual mean sea level pressure for the reconstruction lowers the skill simply due to the seasonal geographical shift of the circulation modes. Our reconstructions based on ice core data also show skill for the North Atlantic sea surface temperatures, in particularly the northern latitudes during winter. In addition, the main modes of the sea surface temperature variability are qualitatively captured by the reconstructions. When testing the skill of the reconstructions using 19 ice cores compared to the ones using 8 ice cores we do not find a clear advantage of using a larger data set. This could be due to a more even spatial distribution of the 8 ice cores. However, including European tree-ring data to further constrain the summer temperature reconstruction clearly improves the skill for this season, which otherwise is more difficult to capture than the winter season.