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Variability and long-term climate change in the Baltic Sea region is investigated for the pre-industrial period of the last millennium. For the first time dynamical downscaling covering the complete millennium is conducted with a regional climate model in this area. As a result of changing external forcing conditions, the model simulation shows warm conditions in the first centuries followed by a gradual cooling until ca. 1700 before temperature increases in the last centuries. This long-term evolution, with a Medieval Climate Anomaly (MCA) and a Little Ice Age (LIA), is in broad agreement with proxy-based reconstructions. However, the timing of warm and cold events is not captured at all times. We show that the regional response to the global climate anomalies is to a strong degree modified by the large-scale circulation in the model. In particular, we find that a positive phase of the North Atlantic Oscillation (NAO) simulated during MCA contributes to enhancing winter temperatures and precipitation in the region while a negative NAO index in the LIA reduces them. In a second step, the regional ocean model (RCO-SCOBI) is used to investigate the impact of atmospheric changes onto the Baltic Sea for two 100 yr time slices representing the MCA and the LIA. Besides the warming of the Baltic Sea, the water becomes fresher at all levels during the MCA. This is induced by increased runoff and stronger westerly winds. Moreover, the oxygen concentrations in the deep layers are slightly reduced during the MCA. Additional sensitivity studies are conducted to investigate the impact of even higher temperatures and increased nutrient loads. The presented experiments suggest that changing nutrient loads may be more important determining oxygen depletion than changes in temperature or dynamic feedbacks.

The analog method (AM) has found application to reconstruct gridded climate fields from the information provided by proxy data and climate model simulations. Here, we test the skill of different setups of the AM, in a controlled but realistic situation, by analysing several statistical properties of reconstructed daily high-resolution atmospheric fields for Northern Europe for a 50-yr period. In this application, station observations of sea-level pressure and air temperature are combined with atmospheric fields from a 50-yr high-resolution regional climate simulation. This reconstruction aims at providing homogeneous and physically consistent atmospheric fields with daily resolution suitable to drive high resolution ocean and ecosystem models. Different settings of the AM are evaluated in this study for the period 1958–2007 to estimate the robustness of the reconstruction and its ability to replicate high and low-frequency variability, realistic probability distributions and extremes of different meteorological variables. It is shown that the AM can realistically reconstruct variables with a strong physical link to daily sea-level pressure on both a daily and monthly scale. However, to reconstruct low-frequency decadal and longer temperature variations, additional monthly mean station temperature as predictor is required. Our results suggest that the AM is a suitable upscaling tool to predict daily fields taken from regional climate simulations based on sparse historical station data.

Coastal locations are highly influenced by input from freshwater river runoff, including sources of terrestrial carbon, which can be expected to modify the 14C reservoir age, or R (t), associated with marine water. In this Baltic Sea case study, pre-bomb museum collection mollusc shells of known calendar age, from 30 locations across a strategic salinity transect of the Baltic Sea, were analysed for 14C, δ13C and δ18O. R (t) was calculated for all 30 locations. Seven locations, of which six are within close proximity of the coast, were found to have relatively higher R (t) values, indicative of hard-water effects. Whenever possible, the Macoma genus of mollusc was selected from the museum collections, in order to exclude species specific reservoir age effects as much as possible. When the Macoma samples are exclusively considered, and samples from hard-water locations excluded, a statistically significant correlation between Macoma R (t) and average salinity is found, indicating a two end-member linear mixing model between 14Cmarine and 14Crunoff. A map of Baltic Sea Macoma aragonite R (t) for the late 19th and early 20th centuries is produced. Such a map can provide an estimate for contemporary Baltic Sea Macoma R (t), although one must exercise caution when applying such estimates back in time or to 14C dates obtained from different sample material. A statistically significant correlation is found between δ18Oaragonite and Macoma R (t), suggesting that δ18Oaragonite can be used to estimate Macoma palaeo-R (t), due to the δ18Oaragonite signal being dominated by the salinity gradient of the Baltic Sea. A slightly increased correlation can be expected when δ18Oaragonite is corrected for temperature fractionation effects. The results of this Baltic Sea case study, which show that R (t) is affected by hydrographic conditions and local carbon inputs, have important consequences for other coastal and estuarine locations, where R (t) is also likely to significantly vary on spatial and temporal bases.