Ice winter severity in the western Baltic Sea in the period of : comparison with other relevant data

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 31: (2011) Published online 19 April 2011 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.2337 Ice winter severity in the western Baltic Sea in the period of : comparison with other relevant data Natalija Schmelzer* and Jürgen Holfort Bundesamt für Seeschifffahrt und Hydrographie, Neptunallee 5, Rostock, Germany ABSTRACT: A 700-year time series of ice winter severity index (referred to as Ice Index in this contribution) which additionally includes the period from 1301 to 1500 is now available for the region of the western Baltic. It is compared with air temperature time series, NAO, and other relevant indices. As expected for a regional time series, agreement with larger-scale northern hemisphere temperature variations is found only on longer time scales. Very good correspondence, even on shorter time scales, is obtained using a geographically close time series of annual mean air temperatures for the Low Countries (The Netherlands and Belgium). Although the data basis for the additionally included period is quite sparse, there is also very good correspondence in this period. Correlation with the NAO Winter Index is generally negative, but the correlation is not significant for earlier periods. Copyright 2011 Royal Meteorological Society KEY WORDS sea ice; climatology; oceanography; Baltic Sea Received 6 July 2009; Revised 7 September 2010; Accepted 4 March Introduction Climate descriptions of particular regions or, on a global scale, climate modelling or data reconstructions backdated to 1500 are found in various studies. More recent studies covering the Baltic Sea have been conducted by Eriksson et al. (2007) and by Hansson and Omstedt (2007). Climate-relevant time series from earlier years are scarce and poorly documented. This study investigates the ice winter severity indices in the region of the western Baltic Sea (cf. Figure 1) in the period of , formerly reconstructed by Koslowski (2004) and analysed by Koslowski and Schmelzer (2007), by comparing the Ice Indices with temperature and other relevant time series available for this period. The ice winter severity index used for the period is based on the following seven ice winter types: very weak and weak (index = 0), moderate (0.1), moderate + (0.3), strong (0.5), strong + (1), very strong (2), and extremely strong (3) ice winters. Use of historical sources is analogous to that in the reconstruction for the period (Koslowski and Glaser, 1995, 1999). An analysis of variations in ice winter severity in the western Baltic in the period of had been made analogous to a study of data series covering the period The low reliability of reconstructed ice winter severity in the period from 1301 to 1400 does not allow data to be treated in the usual way. A linear relationship between the number of anomalous ice winters (index 1) causing complete ice coverage of the * Correspondence to: Natalija Schmelzer, Bundesamt für Seeschifffahrt und Hydrographie, Neptunallee 5, Rostock, Germany. natalija.schmelzer@bsh.de western Baltic, with ice thicknesses greater than 20 cm, and the ice winter severity index found by Koslowski (Koslowski and Glaser, 1999) was used to calculate the value of the Mean Ice Index for the above period (Koslowski and Schmelzer, 2007). Variations of the Ice Index for the periods of and are shown in the graphs in Figures 2 and 3. Compared to the mean, the Gaussian low-pass filtered time series of the Ice Index with a 40-year cut-off period shows a higher severity in , , and , while periods of lower severity occurred in , , and , cf. Fig. 2. The level of ice winter severity between 1301 and 1400 is continuously below that of the reference period of , and even slightly below that of the late 20th century, cf. Figure Comparison with long time series of air temperature In order to assess with some confidence how representative a comparison is for the earliest period of the Ice Index time series, one also has to consider how good the correspondence is in the better sampled later period. The Ice Index time series is a regional index for the western Baltic, and a comparison with available temperature time series for larger regions like the Northern Hemisphere (Mann et al., 1998; Moberg et al., 2005) shows rough similarities only on longer time scales, cf. Figure 4. The Ice Index has been low during the past 150 years, as it was in the period prior to 1500, in agreement with warmer periods found in the temperature time series for Copyright 2011 Royal Meteorological Society

2 ICE WINTER SEVERITY IN THE WESTERN BALTIC SEA 1095 Figure 1. Map of the Western Baltic showing the region under discussion. Place names shown in the map have been mentioned in historical documents used for the reconstruction. Figure 2. Variations of the Ice Index from 1401 to The solid curve represents the smoothed ice winter numerals obtained by applying a Gaussian low-pass filter with a 40-year cut-off period. The dark areas denote the period of increased ice winter severity relative to the long-term mean (horizontal solid line). Figure 3. Variations of Mean Ice Index during the period calculated from the number of anomalous ice winters per decade for time intervals of 19 years. The period of contains 16 years. The areas above the horizontal solid line denote periods with a higher number of anomalous ice winters relative to the long-term mean. the Northern Hemisphere. Similarly, high Ice Indices for the period of correspond to the end of the Little Ice Age. It is not unexpected that correspondence is not so good on shorter time scales. Looking back at the past several well sampled decades and at the different local time series used in the hemispheric reconstruction, it is obvious that there are strong regional differences. Taking a closer look at the different time series used by Osborn and Briffa (2006), we can identify some time series that are better correlated with the Ice Index than others. Of the three time series that are geographically closest to the western Baltic one time series based on tree rings from northern Sweden, another one from the Austrian Alps, and a third time series from the Low Countries (The Netherlands and Belgium) the last one shows the best correlation with the Ice Index series in the better sampled last 500 years, cf. Figure 5. This is quite plausible because the Low Countries lie in a zone where weather patterns resemble those in the western Baltic Sea. Winter weather is mostly influenced by low-pressure systems from the Atlantic Ocean tracking east across The Netherlands, Belgium, and the western Baltic. In both regions, it is the location of high-pressure systems over northern Europe which determines the severity of winters, with more or less intensive ice production. Conspicuous features are the high temperatures associated with the low Ice Index in the first half of the 18th century and colder temperatures before (around 1684) and after (around 1800) this period, when the severity of ice winters in the western Baltic was above average. Good

3 1096 N. SCHMELZER AND J. HOLFORT Figure 4. Comparison of 31-year smoothed Ice Indices in the period with Northern Hemisphere mean annual temperatures (Moberg, 2005, period ). Both data series show differences from the long-term means. Figure 6. Comparison of 31-year smoothed Ice Indices (period ) with Winter Indices for the Low Countries (Van Engelen, 2001, period ). Both data series show differences from the long-term means. Figure 5. Comparison of 31-year smoothed Ice Indices in the period with annual temperature means in the Low Countries (Osborn and Briffa, 2006, period ). Both data series show differences from the long-term means. Figure 7. Comparison of 21-year smoothed Ice Indices with annual temperature means in the Low Countries (Osborn and Briffa, 2006). Both data series are normalized over the period correlation has also been found on shorter time scales, but there also are discrepancies which may be due to the fact that the Low Country temperatures used are annual means. The time series from the Low Countries includes not only the annual mean temperatures: the data from 1500 have been separated into a Summer Index and a Winter Index. A comparison of Winter Indices with the Ice Index is better than with the annual temperatures (Figure 6). Correspondence is often found even on shorter time scales, as for example, an increase in the indices around 1940 and a drop around 1960, but also in the distant past, e.g. the peak in 1558 and the drop in The divergence of both curves in the most recent data is due to the fact that the Winter Index has no theoretical limit, whereas the Ice Index has a lower limit. However, as the Winter Index is not available for the period prior to 1500, the comparison has to be made with annual temperatures, always considering that correspondence cannot be expected on very short time scales. Taking into account the scarcity of historic records on which both time series are based, there is remarkable correspondence between the Ice Index (Ice Indices are available as numerals for the period since 1375, and as Mean Ice Indices since 1301) and the temperature time series. Periods with relatively few strong ice winters Figure 8. Comparison of Mean Ice Indices with annual temperature means in the Low Countries (Osborn and Briffa, 2006). Both data series are normalized over the period (around 1330 and 1380) correspond with warmer-thannormal periods in the Low Countries, and the period of strong ice winters around 1430 corresponds with the lowest annual temperatures in these two centuries, cf. Figures 7 and Comparison with the Sea Ice Index for the southern Baltic Winter severity indices, which are available for other Baltic Sea areas, are rare for the period of The Sea Ice Index for the southern Baltic introduced

4 ICE WINTER SEVERITY IN THE WESTERN BALTIC SEA 1097 by Sztobryn et al. (2009) agrees very well with the Ice Indices for the western Baltic in the period of : the correlation coefficient is Full correspondence cannot be expected because of different climatological, meteorological and hydrological conditions in the two regions. However, the analogy is rather poor for the period of (Sztobryn, 2007). In the Gulf of Gdansk, the severity of only 51 winters in the 15th century has been classified with certainty. During this period, 14 ice winters were very mild, 20 were severe to very severe, and 17 were mild to moderate. All 100 winters in the western Baltic in the period of have been classified with regard to the possible margin of error (cf. Figure 9). Most uncertainties concern mild and very mild ice winters because it has been found impossible to distinguish them clearly. It was concluded in the above paper that the majority of severe and very severe winters in the 15th century occurred in the fifties and from the mid-sixties to seventies, that the second decade was without any severe winters, and that the number of very mild winters was higher in the first half of the century analysed. These conclusions are not fully supported with regard to the western Baltic. Correspondence between very severe ice winters in the above regions is much better, though, because historical records naturally contain more information about winters with anomalous ice production. 4. Comparison with variations of the North Atlantic Oscillation (NAO) A high NAO Winter Index is correlated with stronger westerly winds bringing warmer air into the western Baltic region. Therefore, a negative correlation with the Ice Index is to be expected. A comparison of the smoothed (31-year running mean) time series of the Ice Index with the reconstructed NAO Winter Index (Cook, 2003) gives an ambiguous picture, cf. Figure 10. There are times where the expected negative correlation is visible, but there are also times where the correlation is positive, e.g. around As this period is considered to be adequately sampled with respect to both indices, it is likely that a physical process other than the simple one described above is involved on a 31-year time scale. Using the yearly data of the last 100 years, a rather small but nevertheless significant negative correlation ( 0.47) is found. However, there is no significant correlation in the earliest 100 years ( 0.11). In fact, a running correlation over 100 years shows an almost linear trend from the 15th to the 20th century. Similar longer-term trends were found by Jones et al. (2003), although they used different NAO indices which did not extend as far back, and several different surface climate series. The explanation provided by them was either a deterioration in the NAO index covering earlier periods or a gradual change in the nature of the NAO. This may also explain the trend found in this study. There may have been a simple pattern change shifting the whole NAO pattern eastward over time. A more easterly atmospheric pattern would also explain the fact that the correlation between the ice index and Low Country temperatures is better in the 20th than in the 15th century. 5. Conclusions The reconstruction and analysis of ice winter severity for the region of the western Baltic was found to be a suitable method for identifying trends in climatic variation in Europe. Compared with available temperature data series for the time period of , correspondence with temperatures in the Northern Hemisphere exists only on longer time scales due to the different spatial scales used, but there is good correspondence with annual temperatures in the Low Countries, and even better correspondence with the Winter Index of the Low Countries, also on shorter time scales. Despite the scarcity of data available for reconstruction of the Ice Index for the period of , good correspondence has been found also for this period. The scarcity of data thus seems to be no major source of errors. The periods with higher and lower occurrence of severe ice winters were well determined using the Ice Index or Mean Ice Index. The level of ice winter severity between 1301 and 1400 is continuously below that of Figure 9. Ice Index numerals The solid curves represent smoothed Ice Index series obtained by applying a Gaussian low-pass filter with a 20-year cut-off period. The shaded areas denote the possible margin of error (Koslowski and Schmelzer, 2007). Figure 10. Comparison of 31-year smoothed Ice Indices with NAO Winter Indices (Cook, 2003) in the period

5 1098 N. SCHMELZER AND J. HOLFORT the long-term mean and even slightly below that of the late 20th century. Despite the isolated occurrence of very strong and extremely strong ice winters in the 14th century, it may be considered part of the Medieval Warm Period. Although a correlation has been found between the ice index and the NAO for more recent times, there has been no significant correlation for times further past. This may be attributable to the simple fact that in earlier times the NAO pattern was situated further west and did not extend into the western Baltic region. After additionally including the period from 1301 to 1500, a 700-year time series of Ice Index is now available for the region of the western Baltic and can be used for validation of modelled or reconstructed climate-relevant data. References Cook ER Multi-Proxy Reconstructions of the North Atlantic Oscillation (NAO) Index, A Critical Review and a New Well-Verified Winter NAO Index Reconstruction Back to AD In The North Atlantic Oscillation, Hurell JW, Kushnir Y, Ottersen G, Visbeck M (eds). American Geophysical Union: Washington, DC; Eriksson C, Omstedt A, Overland JE, Percival DB, Mofjeld HO Characterizing the European Sub-Arctic Winter Climate since 1500 Using Ice, Temperature and Atmospheric Circulation Time Series. Journal of Climate 20: , DOI: /2007JCLI Hansson D, Omstedt A Modelling the Baltic Sea ocean climate on centennial time scale: temperature and sea ice. Climate Dynamics 30(7 8): , DOI: /s Jones PD, Osborn TJ, Briffa KR Pressure-based measures of the North Atlantic Oscillation (NAO): A comparison and an assessment of changes in the strength of the NAO and in its influence on surface climate parameters. In The North Atlantic Oscillation, HurellJW, Kushnir Y, Ottersen G, Visbeck M (eds). American Geophysical Union: Washington, DC; Koslowski G, Glaser R Reconstruction of the Ice Winter Severity since 1701 in the Western Baltic. Climate Change 31: Koslowski G, Glaser R Variations in Reconstructed Ice Winter Severity in the Western Baltic from 1501 to 1995, and their Implications for the North Atlantic Oscillation. Climate Change 41: Koslowski G Eisvorkommen in der westlichen Ostsee im Zeitraum , tungen/eis/eiswinterindex.pdf. Koslowski G, Schmelzer N Ice Winter Severity in the Western Baltic Sea in the Period Berichte des BSH 42: Mann ME, Bradley RS, Hughes MK Global-scale temperature patterns and climate forcing over the past six centuries. Nature 392: Moberg A, Sonechkin DM, Holmgren K, Datsenko NM, Karlén W Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data. Nature 433(7026): Moberg A Year Northern Hemisphere Temperature Reconstruction, ftp,//ftp.ncdc.noaa.gov/pub/data/paleo/contributions by author/moberg2005/nhtemp-moberg2005.txt, IGBP PAGES/World Data Center for Paleoclimatology, Data Contribution Series # , NOAA/NGDC Paleoclimatology Program, Boulder CO, USA. Osborn TJ, Briffa KR The spatial extent of 20th century warmth in the context of the past 1200 years. Science 311: Osborn TJ, Briffa KR Spatial Extent of Warm and Cold Conditions over the Northern Hemisphere Since 800 AD, IGBP PAGES/World Data Center for Paleoclimatology, Data Contribution Series # , NOAA/NCDC Paleoclimatology Program, Boulder CO, USA. Sztobryn M Sea Ice Conditions in Gulf of Gdansk in 15 century. Berichte des BSH 42: Sztobryn M, Schmelzer N, Vainio J, Ericsson P Sea Ice Index, Proceedings of the Sixth Workshop on Baltic Sea Ice Climate, Report Series in Geophysics of the University Helsinki, Van Engelen AFV, Buisman J, Ijnsen F A millennium of weather, winds and water in the Low Countries. In History and Climate: Memories of the Future?, Jones PD et al., (eds). Kluwer Academic/Plenum Publishers: New York, Boston, Dordrecht, London, Moscow;