Abstract. The North Atlantic region is a key component of the climate system via large-scale atmosphere and ocean circulation. Climate field reconstructions can provide a long-term context for ongoing climate change and contribute to our understanding of climate dynamics, impact of external forcings, and act as references for model evaluation and baseline for natural variability. There are distinct differences in North Atlantic climate variability between the seasons in terms of climate modes and amplitude of the variance. Constraining long-term climate variability in sub-annual resolution is therefore needed for a more complete understanding of the governing processes. In this study, we present reconstructed climate in annual and seasonal resolution based on a small high-quality network of proxy data combined with output from an isotope enabled climate model. Compared to earlier work, we have improved the methodology to obtain better skill across a larger area and more realistic variance of the reconstructed variables which include 2 m temperature (T2m), sea surface temperature (SST), sea level pressure (SLP) and precipitation amount. Here we validate the reconstructions against reanalysis data, observed SST and eight long-term records of observed temperature. The reconstructed temperature correlates with up to 0.71 for seasonal data and 0.68 for annual data compared to reanalysis data. The skill for SLP shows the imprint of large-scale circulation for winter with more local patterns dominating for summer. This is also mirrored in the skill for precipitation. In addition, the reconstructed annual mean SST shows basin-wide skill for the North Atlantic, indicating relevance of the reconstruction to studies of atmosphere-ocean interaction. A comparison to other climate field reconstructions show that our new reconstruction has comparable properties, and is unique in offering long-term seasonal SLP, temperature and precipitation. This comparison also underlines the importance of consistency in choice of assimilated proxy data, which influences the long-term performance of the reconstruction. In summary, the results show the potential of assimilating a small high-quality network of proxy records.
Sjolte et al. (Mon,) studied this question.