Report Series of the Finnish Institute of Marine Research. SEA ICE

SEA ICE AND RELATED DATA SETS FROM THE BALTIC SEA AICSEX — METADATA REPORT

Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen

Merentutkimuslaitos SCATTER DIAGRAM WAVE STATISTICS FROM THE NORTHERN Havsforskningsinstitutet BALTIC SEA Finnish Institute of Marine Research Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi

ASSESSMENT — STATE OF THE GULF OF FINLAND IN 2002

Matti Perttilä (Editor)

SHORT-TERM EFFECTS OF NUTRIENT REDUCTIONS IN THE NORTH SEA AND THE BALTIC SEA AS SEEN BY AN ENSEMBLE OF NUMERICAL MODELS

Tapani Stipa, Morten Skogen, Ian Sehested Hansen, Anders Eriksen, Inga Hense, Anniina Kiiltomäki, Henrik Soiland & Antti Westerlund

Report Series of the Finnish Institute of Marine Research.

MERI — Report Series of the Finnish Institute of Marine Research No. 49, 2003

SEA ICE AND RELATED DATA SETS FROM THE BALTIC SEA AICSEX — METADATA REPORT Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen

SCATTER DIAGRAM WAVE STATISTICS FROM THE NORTHERN BALTIC SEA Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi

ASSESSMENT — STATE OF THE GULF OF FINLAND IN 2002 Matti Perttilä (Editor)

SHORT-TERM EFFECTS OF NUTRIENT REDUCTIONS IN THE NORTH SEA AND THE BALTIC SEA AS SEEN BY AN ENSEMBLE OF NUMERICAL MODELS Tapani Stipa, Morten Skogen, Ian Sehested Hansen, Anders Eriksen, Inga Hense, Anniina Kiiltomäki, Henrik Soiland & Antti Westerlund MERI — Report Series of the Finnish Institute of Marine Research No. 49, 2003

Cover photo by Riku Lumiaro.

Publisher: Julkaisija: Finnish Institute of Marine Research Merentutkimuslaitos P.O. Box 33 PL 33 FIN-00931 Helsinki, Finland 00931 Helsinki Tel: + 358 9 613941 Puh: 09-613941 Fax: + 358 9 61394 494 Telekopio: 09-61394 494 e-mail: [email protected] e-mail: [email protected]

Copies of this Report Series may be obtained from the library of the Finnish Institute of Marine Research.

Tämän raporttisarjan numeroita voi tilata Merentutkimuslaitoksen kirjastosta.

ISSN 1238-5328 Dark Oy Vantaa 2003 CONTENTS

SEA ICE AND RELATED DATA SETS FROM THE BALTIC SEA AICSEX — METADATA REPORT 3 Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen

SCATTER DIAGRAM WAVE STATISTICS FROM THE NORTHERN BALTIC SEA 15 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi

ASSESSMENT — STATE OF THE GULF OF FINLAND IN 2002 33 Matti Perttilä (Editor)

SHORT-TERM EFFECTS OF NUTRIENT REDUCTIONS IN THE NORTH SEA AND THE BALTIC SEA AS SEEN BY AN ENSEMBLE OF NUMERICAL MODELS 43 Tapani Stipa, Morten Skogen, Ian Sehested Hansen, Anders Eriksen, Inga Hense, Anniina Kiiltomäki, Henrik Soiland & Antti Westerlund

SEA ICE AND RELATED DATA SETS FROM THE BALTIC SEA AICSEX - METADATA REPORT

Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen

Finnish Institute of Marine Research, P.O. Box 33, FIN-00931 Helsinki, Finland

ABSTRACT

The goals of the Arctic Ice Cover Simulation Experiment (AICSEX) are to assess and study the variability and trends of selected climatic parameters during the 20th century, to model these processes, to assess by model simulations the impact of possible melting of the ice cover and to assess the economic impact of these changes to marine transportation. One part of these studies focuses on the variations and possible changes in the Baltic Sea, a small semi-enclosed sea that is heavily affected by the climate and weather conditions of the Northern Atlantic and Arctic Seas. Most relevant data sets from the northern Baltic Sea have been collected for analysis and these data sets will be introduced in this meta-data report. Key words: sea ice, Baltic Sea, data, inventory, long-term data

1. INTRODUCTION Many recent analyses have indicated changes in the ice cover of the Arctic Oceans and adjacent areas. If the ice cover of the Arctic Ocean would diminish in a considerable amount, it would possibly have drastic effects on the climate of the Northern Europe, too. The Baltic Sea is a small semi-enclosed sea area in the Northern Europe (Fig. 1). The environmental conditions of the Baltic Sea depend heavily on the climate and weather conditions of the North Atlantic and Arctic Oceans. One example is the water exchange between the world ocean and the Baltic Sea. Major saline water inflows to the Baltic Sea are infrequent events that depend on the favourable wind conditions in the autumn and in the winter. The water exchange determines the stratification of the Baltic Sea and thus the amount of heat stored in the sea in summer months, too. This reflects to the ice conditions in the next winter. The northern parts of the Baltic Sea, The Gulf of Bothnia (that consists of Bothnian Bay, The Quark and Bothnian Sea) and the Gulf of Finland, have ice cover at least on the coasts every winter. Ice is a very important regulating parameter for the winter navigation especially for Finland, Sweden and St Petersburg area of Russia. Practically all Finnish harbours are ice covered every winter. The average length of the ice season varies from 90 days in the southern coasts of Finland to over 190 days in the northern end of the Bothnian Bay. In the Eastern end of the Gulf of Finland, in St Petersburg area, the average length of the ice season is over 140 days. Because of its great importance for the foreign trade, much effort has been put to the observations of the ice conditions already over a century. In this meta-data report we introduce shortly several time series that we consider valuable and useful for the analysis of the ice conditions and their variability in the Baltic Sea. The obvious link between the Baltic Sea ice conditions and the conditions in Northern hemisphere is the NAO.

4 Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen MERI No. 49, 2003

Fig. 1. The location of the Baltic Sea and names of some of its parts.

2. AVAILABLE CLIMATOLOGICAL AIR TEMPERATURE AND SEA ICE DATA USED AT FIMR IN AICSEX PROJECT

An important background parameter in the analysis of sea ice conditions is the air temperature. Good correlation between the Baltic Sea ice conditions and regional or local air temperature has been shown in many earlier studies. Some relatively long time series of air temperature around the northern Baltic Sea are available. We have chosen a couple of these (see Table 1 and Fig. 2) as basic local to regional background information of the ice conditions for especially the northern parts of the Baltic Sea where the ice conditions are most severe in the area and sea ice exists every winter. Two of these time series are from larger cities (Stockholm and Helsinki) and are as such subject to urbanisation effects. The originators of the time series have done careful work to eliminate urbanisation effects from the data (Raino Heino, Finnish Meteorological Institute, personal communication).

Table 1. Air temperature time series from the Baltic Sea area used in the AICSEX study.

Site Time series type Time series duration

Helsinki Monthly mean Jan-Dec 1829-2000 Mariehamn Monthly mean Jan-Dec (1881) 1885-2000 Haparanda Monthly mean Jan-Dec 1860-2000 Stockholm Monthly mean Jan-Dec 1756-2000 Sea ice and related date sets from the Baltic Sea — AICSEX — metadata report 5

15" 17° 19° 21° 22' 25° 27° 29° 67'-

Haparanda -66° 66°-

64°-

63 Finland -62° 62'-

-61' 61` Helsinki -60° Sweden 60°,- Stockholm 59°-

58°-

57°-

56°-

55°-

54°- 13° 15° 17° 19' 21° 23' 25° 27` 2p.

Fig. 2. Location of the air temperature observation stations in the Baltic Sea area used in AICSEX study.

3. MAXIMUM ANNUAL ICE EXTENT OF THE BALTIC SEA

Maximum annual ice extent of the Baltic Sea is a parameter that has often been used as a good indicator of the severity of the winter in the Baltic Sea. This parameter describes the area of the ice cover in square kilometres in one single situation during the winter when the ice cover had its largest extent. The duration of this "event" may vary depending on the winter, but it is not included as a parameter. The total area of the Baltic Sea is in this context counted to be 420 000 km2. Time series of the maximum annual ice extent has been constructed from winter 1719/1720 onwards. Risto Jurva from Finnish Institute of Marine Research (see Seinä & Palosuo 1996) constructed the early part of the time series using many different types of data sources to estimate the severity of the winter and thus the ice extent. Later on the Finnish Ice Service has determined the value of this parameter for every winter on the basis of regular observations of different types. Today this parameter is a well-observed one and is based on direct remote sensing observations. The time series is updated every spring (Seinä & al. 2001) and is today 282 years long. The maximum ice extent occurs in mid winter and thus the exact year of the occurrence of the situation is inarked as the year value in the time series (e.g. winter 1719/1720 is marked 1720). In spite of the internal heterogeneity of the time series, it has been used in many studies (Alenius & Makkonen 1981) and is considered to be a useful data set.

Table 2. Maximum annual ice extent time series of the Baltic Sea.

3.1.1 Parameter Seasons No of years

Maximum ice extent 1720-2001 282

The time series of the maximum annual ice extent of the Baltic Sea shows very large variations from year to year (Fig. 3). The values have so far been between 52 000 km2 and 420 000 km2. Median value is 180 000 km2, average value 215 000 km2 and standard deviation 114 000 krn2. From the variability it can be seen that the memory of the system is shorter than one year. Thus an extremely hard winter can follow an extremely mild winter and vice versa. By smoothing the time series some general tendencies 6 Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen MERI No. 49, 2003 can be found. The winters were on the average harder in the end of 1700's and beginning of 1800's than in the 1900's. One thing to be noted in the time series of the maximum annual ice extent of the Baltic Sea is that the process is not linear. This is because of the fact that the Baltic Sea has several sub-basins and the freezing begins from the coasts.

Maximum annual ice extent in the Baltic Sea. Annual values and 30y running average. 500

460

400

350

., 300 E 8 250 O_ 200

150

100

0 1700 1750 1$00 1850 1900 1950 2000 year

Fig. 3. Time series of the maximum annual ice extent of the Baltic Sea.

4. ICE AND SNOW THICKNESS: MONTHLY AVERAGES

Ice and snow thickness has been measured at several locations on a more or less regular basis. Though the time series are not complete, they can be used as a valuable material for analysis of changes in ice conditions from year to year and from decade to decade. We have chosen several such time series for analysis in AICSEX study (see Table 3 and Fig. 4). The length of these time series varies from about half a century to little more than a century.

Table 3. Ice and snow thickness time series used in AICSEX study. The ice seasons are marked according to spring (e.g. winter 1996-1997 is denoted as 1997).

Station Location Seasons No of years Ajos 65°43,9'N 24°33,4'E 1912-16, 1918-26, 1928-2001 88 Vaasa 63°05,0'N 21°36,0'E 1901, 1903, 1907-08, 1914-15, 1919-74 68 Rauma 61°08,3'N 21°20,6'E 1913-17, 1919-49, 1951-65, 1978-2001 75 Jungfruskär 60°08,5'N 21°04,0'E 1915, 1921-66 45 Helsinki 60°10,2'N 24°58,1'E 1890-2001 1 1 l Loviisa 60°22,5'N 26°18,1'E 1914-17, 1919-45, 1949-50, 1953-70, 1974-2001 80

Sea ice and related date sets from the Baltic Sea — AICSEX — metadata report 7

9° 11° 13° 15°° 17° 13' 21° 23' 25' 27° 23° -67' 67°-

Ajos -66° 66°-

-65° p : 65°-

-64' 64"-

"'63° asa 63'-

-62° 62°- Rauma

-61' Loviisa 61°- 1 -60' Jungfruskar 60`r Helsinki ~}`~ f •59° 59°•

58' 58°-

57 57'-

56° 56°-

55' 55°-

-54' 54' 13° 15' 17° 19° 21° ° 25° 27° 29°

Fig. 4. Locations of the observation sites of ice and snow thickness.

5. DATES OF FREEZING, BREAK-UP, NUMBER OF ICE DAYS AND MAXIMUM ICE THICKNESS The dates of freezing and break-up of ice are continuously recorded at the coastal sites where also ice thickness measurements are done. From the freezing date and break-up date of the ice the number of ice days is easy to calculate. These time series describe well the character of the ice conditions.

17 19 21° 23° 25 27 2

66° KemiAlos 66°

-65° Oulu,Saapaskari 65'

.64° Ilrkokkola,T ankar 64' VaasaHorrsk2r

6 Vaasa,Vaskiluoto 63°

Kaskinen -62' 62' Rauma 61 61° Turku Loviisa Helsinki +Jun 0 rengrun -60' tviarieharnn Låskar Utö -59° Bogskär 59'

58° 58°1

29' 1

Fig. 5. Observation stations for dates of freezing and break-up of ice as well as number of ice days and maximum ice thickness. 8 Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen MERI No. 49, 2003

Table 4. Time series of dates of freezing, break-up, number of ice days and maximum ice thickness (+ H). The ice seasons are marked according to spring (e.g. winter 1996-1997 is denoted as 1997).

Station Location Seasons Notes Kemi: Ajos 65°39,8'N 24°31,4'E (1890) 1916-2000 + H H 1912-2000, some missing data Oulu: Saapaskari 65°03,2'N 25°l0,7'E 1915-2000 Kokkola: Tankar 63°57,0'N 22°51,0'E (1980) 1915-2000 + H A lot of missing thickness data. Vaasa: Vaskiluoto 63°04,9'N 21°33,2'E (1890) 1915-2001 + H H 1921-74,1982-88. Local effects. Norrskär 63°14,0'N 20°36,2'E 1915-2000 Kaskinen: Harbour 62°22,TN 21°13,2'E 1915-2000 + H H 1915-34, 1946-50, 1966-67, 1987-2000 Rauma: Harbour 61°07,6'N 21°27,6'E (1891) 1915-2000 + H H 1915-65,1978-2000 Mariehamn: Harbour 60°05,4'N 19°56,1'E 1891-2000 Lågskär 59°50,5'N 19°54,9'E 1919-2000 Turku: Harbour 60°26,2"N 22°13,4'E 1899-2000 Bogskär (Kihti) 60°04,5'N 20°55,6'E 1915-2000 + H H 1921-1966 Utö 59°46,9"N 21°22,4'E 1890-2000 Bogskär (N Baltic) 59°30,4'N 20°21,2'E 1915-2000 Hanko: Harbour 59°48,7'N 22°55,1'E 1891-2000 Helsinki: Harbour 60°08,9'N 24°55,2'E (1830) 1861-2000 + H Local urbanization? Kalbådagrund 59°59,2'N 25°36,2'E 1915-2000 Loviisa: Harbour 60°24,7'N 26°15,6'E (1896) 1915-2000 + H Orrengrund 60°16,4'N 26°26,4'E 1915-2000

17° 19' 21' 23' A' 27° 29'

-66' KemiAjos 66°-

-65' Oulu,Saapaskari 65°-

-64' ~ Kokkola,Tankar 64°- Va.3sa,hlorrskär

-63' Vaasa,Vaskiluoto 63°-

Kaskinen -62' 62°-

Raurna -61° 61°- Turku Loviisa Helsinki r4J un fruskär ,Orrengrun -60' Mariehamn 60°- Kalb dagrundf Lagskär Hanko Utö y -59' Bogskär 59°-

-58' 58°-

21' 27° 29°

Fig. 5. Observation stations for dates of freezing and break-up of ice as well as number of ice days and maximum ice thickness. Sea ice and related date sets from the Baltic Sea — AICSEX — metadata report 9

Length of the ice season (10-y. averages) on the Finnish coast 200

180

160

140

120 cn ~ ~ -° 100

20 1920 1930 1940 1950 1960 1970 1980 1990 2000 year

Fig. 6. Time series of the length of the ice season at different locations at the coast of Finland.

Maximum ice thickness in Helsinki harbour. Annual (red) and 10y running average (blue). 90

~ 50 ~ C 1 L" 40 4) U 30

0 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 year

Fig. 7. Maximum ice thickness in Helsinki harbour. 10 Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen MERI No. 49, 2003

Maximum annual ice thickness on the Finnish coast. 10 year running averages. 90

E •`=.' 60

~ 50

20 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 year

Fig. 8. Maximum annual sea ice thickness at some locations around Finland in the Gulf of Bothnia.

Maximum annual ice thickness on the Finnish coast. 10 years running averages. 90

20 1910 1920 1930 1940 1950 1960 19713 19813 1990 2000 year

Fig. 9. Maximum annual sea ice thickness at some locations around Finland in the Gulf of Finland.

6. EXTENT OF ICE COVER OUTSIDE VARIOUS FINNISH HARBOURS

On interesting measure of the ice conditions is the distance from the harbour to the ice edge or to the thin ice (Seinä 2001). This parameter shows directly the navigational effect of the ice conditions. It has been observed from most of the important harbours of Finland. The measurements are done using nautical miles as the distance unit. Sea ice and related date sets from the Baltic Sea — AICSEX — metadata report 11

Table 5. Time series of the distance from the harbour to the edge of open water and the distance of thick ice (of more than 10 cm) in nautical miles.

Harbour Ice Season

Kemi, Oulu, Raahe, Kokkola, Pietarsaari, 1931-2000 Vaasa, Kaskinen, Pori, Rauma, Uusikaupunki, Between Oct 15 — Jun 17, every 5th day. Mariehamn, Turku, Hanko, Helsinki, Loviisa, Kotka, Hamina

Distance to the edge of free water from Hanko harbour. Annual max. 300

250

200

w ~m Tsi 150

m c

100 -

0 ~ \ ~ 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 red = ice blue = thick (10cm ) ice

Fig. 10. Maximum annual distance from the Hanko harbour to the ice edge.

7. A DATA BANK FOR BALTIC SEA ICE AND SEA SURFACE TEMPERATURES — BASIS

The Finnish and Swedish national navigation authorities have supported ice studies through the Winter Navigation Research Board. In early 1980's this board funded a joint Swedish-Finnish project to construct a comprehensive digital data bank for sea ice and surface temperature for the Baltic Sea (Udin &al. 1981). This data bank includes gridded data of numerical codes describing both quantitative and qualitative information on the sea ice. The grid sides are 30 minutes along parallels and 15 minutes along meridians. The ice code contains information on ice type, concentration and dominant thickness where there is ice and surface temperature where there is no ice. Two published ice atlases are based on that data set: Climatological Ice Atlas (1982) and Phases of the Ice Season in the Baltic Sea (Leppäranta & al. 1988). The data cover winters from 1963/1964 to 1978/1979. The original idea was to continue the series to cover period 1960-1990. At present the data are under update work in the Swedish Meteorological and Hydrological Institute. 12 Pekka Alenius, Ari Seinä, Jouko Launiainen & Samuli Launiainen MERI No. 49, 2003

8. NORTH ATLANTIC OSCILLATION, NAO The sea ice conditions of the Baltic Sea depend largely on the weather conditions coming to the Baltic Sea area from the North Atlantic. Thus it is most natural to study the relationship between different ice parameters in the Baltic Sea and the North Atlantic Oscillation (NAO). In our studies within AICSEX we have used the wintertime NAO describing the surface pressure difference between Ponta Delgada, Azores and South Western Iceland (Hurrell 1995).

NAO Years Notes Azores (Ponta Delgada ) — 1865-1997 Surface pressure difference (units: mbar x 10). SW Iceland Monthly mean, annual mean and wintertime (Dec—Mar) mean.

Surface pressure difference between Azores (Ponta Delgada) — SW Island. (Hurrell 1995). Data and discussion on NAO is available from http://www.cru.uea.ac.uk/cru/data/nao.htm.

1 /intertirne (Dec -Mar) NAO. Azores (Ponta Delgada) - SW Iceland. 3

-2

red = annual, blue = 10 y running average

1880 1900 1920 1940 1960 1980 year

Fig. 11. Time series of wintertime NAO with 10 year floating averages smoothing.

9. CONCLUDING REMARKS The sea ice conditions are extremely important especially for Finland because most of the foreign trade of the country goes by ships and practically every harbour is ice covered every winter. Therefore it is natural that ice conditions have been followed and studied in Finland in a close co-operation with neighbouring countries, Sweden, Russia and Estonia. The observation activities arose to systematic studies in the advent of icebreakers about a century ago. The methods of observations have changed during the century in many ways. Coastal observations and sparse observations from ships are complemented by today's remote sensing with satellite radars. Though the old observations are important in studies of the natural variability and possible changes in the wintertime conditions in the Sea ice and related date sets from the Baltic Sea — AICSEX — metadata report 13

Baltic Sea. Those time series that are introduced in this meta-data report are those that has been and will be used in such studies. Whenever additional data series are available, they could be included as the raw material of the research as well. We consider that the presented data sets are useful and can be used with quite big confidence in studies of global change and the impact of the possible changes to the Baltic Sea navigation, too.

9. REFERENCES

Alenius, P. & Makkonen, L. 1981: Variability of the annual maximum ice extent of the Baltic Sea. — Arch.Met.Geoph.Biokl. Ser.B, 29, 393-398. Climatological Ice Atlas for the Baltic Sea, Kattegatt, Skagerrak and Lake Vänern (1963-1979), 1982: Swedish Meteorological and Hydrological Institute and Finnish Institute of Marine Research. Hurrell, J.W., 1995: Decadal trends in the North Atlantic Oscillation and relationships to regional temperature and precipitation. — Science 269, 676-679. (See also http://www.cru.uea.ac.uk/cru/ data/nao.htm) Leppäranta, M., Palosuo, E., Grönvall, H., Kalliosaari, S., Seinä, A. & Peltola, J. 1988: Phases of the ice season in the Baltic Sea. — Finnish Mar. Res. No. 254, Suppl. 2. Seinä, A. 2001: Icebreaking operations in the eastern Gulf of Finland: Ice conditions. (unpublished manuscript) Seinä, A., Grönvall, H., Kalliosaari, S. & Vainio, J. 2001: Ice seasons 1996-2000 in Finnish sea areas. — Meri, Report Series of the Finnish Institute of Marine Research, No. 43, pp. 3-98. Seinä, A. & Palosuo, E. 1996: The classification of the maximum annual extent of ice cover in the Baltic Sea 1720-1995. Based on material collected by Risto Jurva (winters 1720-1940) and the material of the Ice Service of the Finnish Institute of Marine Research (winters 1941-1995). — Meri, Report Series of the Finnish Institute of Marine Research, No. 27, pp. 79-91. Udin, I., Uusitalo, S., Sahlberg, J., Seinä, A., Lundqvist, J. -E. & Leppäranta, M. 1981: BASIS — A data bank for Baltic Sea ice and sea surface temperatures. — Winter Navigation Research Board, Swedish Administration of Shipping and Navigation, Finnish Board of Navigation, Res. Rep. No. 34. 14 SCATTER DIAGRAM WAVE STATISTICS FROM THE NORTHERN BALTIC SEA

Kimmo Kahma, Heidi Pettersson & Laura Tuomi

Finnish Institute of Marine Research, P.O. Box 33, FIN-00931 Helsinki, Finland

ABSTRACT This report presents wave statistics and scatter diagrams from the northern parts of the Baltic Sea. The statistics are based on a total of nine years of wave buoy measurements. Two classes of statistics are given, of which one is based directly on the measurements. In the other class data from the average ice- free period is used and the missing values are estimated by seasonal distributions. Scatter diagrams are extrapolated to hundred years' return period using a two dimensional Weibull-Gaussian distribution. Key words: Northern Baltic Sea, wave statistics, wave data

1. INTRODUCTION

The Baltic Sea is relatively small if compared to the size of the Atlantic Ocean. However, the waves in severe storms in North Atlantic are only twice as high as the waves in the middle of the Baltic Sea. Significant wave heights over 7 meters have been measured in the Northern Baltic Sea more than once. Waves of this size are of importance for shipping and for the safety at seas. From September 1996 the Finnish Institute of Marine Research (FIMR) has operated a permanent wave buoy in the Northern Baltic Proper. Before that during the years 1982...1986 FIMR and the Swedish Meteorological and Hydrological Institute (SMHI) operated a wave buoy in another location of the Northern Baltic Proper. By September 2000 these measurements together cover 9 years. The earlier data, has significant gaps whereas the resent data has only few interruptions. This data, as well as wave model calculations have been used to obtain more accurate statistics than have been available in the past. When statistics about the extreme wave conditions are estimated the data still leave considerable uncertainties. The aim in this study is to estimate an upper limit that would be unlikely to be found too low when more data becomes available from future measurements. In 1998 the Finnish Maritime Administration ordered a study called "Passenger ship bow structural design against wave induced loads at the Northern Baltic". As a subcontractor for the VTT Manufacturing Technology the Finnish Institute of Marine Research (FIMR) calculated the wave statistics for the study (Kahma & al. 2000). The present report is an update of the analysis covering data up to September 2000.

2. WAVE CLIMATE IN THE NORTHERN BALTIC SEA

2.1 General

The wave climates in various parts of the Baltic Sea differ greatly. In the Northern Baltic Proper the typical wave height is roughly half of that in the North Atlantic, whereas in the Gulf of Finland the wave height is limited to roughly one half of wave height in the Northern Baltic Proper. The wave climate in the Bothnian Sea is slightly less severe than the wave climate in the Northern Baltic Proper. Besides having a less severe wave climate, the Åland Sea, the Gulf of Finland, and the Quark have-- directionally strongly skewed statistical distributions. In other words, large waves can arrive only from certain directions, and waves tend to arrive from those directions even when the direction of the wind is 16 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

different. This means that the loads for vessels that are dependent on wave direction may vary even more than the non-directional wave statistics would indicate.

2.2 Treatment of missing values and the ice period

It is very difficult to make uninterrupted wave measurements in the open sea. Instrument or transmission failures and ice are the most important reasons for gaps in the record. Ice introduces a special problem in wave statistics, because the period of missing values caused by ice is by no means random. Especially in the Bothnian Bay the long ice season influences the wave climate. The Bay is at least partly frozen during the season when the wind climate is most severe. Annual statistics and maximum wave height give a distorted — too low — view of the wave climate of the ice-free season. When the sea there is ice-free the waves are higher than they are in an area that has the same annual wave statistics but never has ice. This applies to a smaller extent also to the other areas of the Baltic Sea, and therefore has to be taken into account to avoid inconsistencies in comparisons. There are at least four different ways to analyse wave climate in areas where there is ice during part of the year: A. Direct statistics. Only measured data is taken into account. No corrections are made to compensate for the uneven distribution of missing values. B. Formal statistics Wave height in the presence of ice equals zero. Missing values in other seasons are estimated from the seasonal distributions. C. Ice-free season statistics Only the part of the year when the sea would be ice-free in an average winter is taken into account when statistics are calculated. Missing values during the ice-free season are estimated from the seasonal distributions. D. Hypothetical "no ice" statistics Using wave models and wave measurements from winters when the ice period is shorter than normal, hypothetical statistics are calculated to represent the wave climate under the assumption that the sea remains ice-free throughout the year. None of these methods is suitable for all types of applications. In this report we will present data using methods A and C. Note that when time, rather than a percentage of time, is used in a table, statistics of type B are identical with statistics of type C, except for classes that include zero wave height.

2.3 Overview of wave climate in the Northern Baltic Sea

The Finnish Institute of Marine Research (FIMR) has made wave measurements in the Baltic Sea from 1972. The Swedish Meteorological and Hydrological Institute (SMHI) has measured waves from 1979 at Almagrundet and at some other locations, all South from Almagrundet. During the years 1983 — 1986 measurements were carried out at Bogskär as a joint project by FIMR and SMHI. The periods and the locations of the wave measurements by FIMR are shown in Figure 1.

The values of significant wave height H. (defined as H. = H„0 = 4 Al m0 ) given in Table I are the highest actually measured. They are not adjusted for the differences in measuring times or for the differences in the length of the ice season. Table 1 thus represents statistics of type A. It gives a somewhat misleading impression — too low — of the wave climate, especially in the Bothnian Bay. Scatter diagram wave statistics from the northern Baltic Sea 17

Table 1. The highest measured significant wave heights by November 2002.

Northern Baltic Proper (Almagrundet) 7.7 m Northern Baltic Proper (Bogskär) 6.7 m Northern Baltic Proper (permanent wave buoy) 7.4 m Gulf of Finland (Kalbådagrund) 5.2 m Southern Bothnian Sea 5.5 m Bothnian Bay 3.1 m

Table 2. The most probable values of the maximum individual wave height at the measuring point during the wave measurements.

Northern Baltic Proper (Almagrundet) 14.0 m Northern Baltic Proper (Bogskär) 12.0 m Northern Baltic Proper (permanent wave buoy) 13.3 in Gulf of Finland (Kalbådagrund) 9.0 m Southern Bothnian Sea (Sandbäck) 10.0 m Southern Bothnian Bay (Ulkokalla) 5.5 m

The estimates in Table 2 were calculated from the measured H, using the empirical relations proposed by Forristall (1978) and represents wave statistics of type A. In Tables 1-3 the data from the permanent wave buoy in the Northern Baltic Proper cover measurements from September 1996 to September 2000, including two severe storms in December 1999, when 7.4 m significant wave height was measured.

Table 3. Hs exceeded 10% of the ice-free time.

Northern Baltic Proper (permanent wave buoy) 2.5 m

Northern Baltic Proper (Bogskär) 2.7 m

Gulf of Finland (Helsinki - Tallinn) 1.9 m

Southern Bothnian Sea 2.4 m

Southern Bothnian Bay 1.4 m

3. MEASURED WAVE STATISTICS IN THE NORTHERN BALTIC PROPER

3.1 Measured wave statistics from the Northern Baltic Proper wave buoy

Since September 1996 FIMR has made wave measurements by a Directional Waverider buoy at 59°15' N 21°00' E, in the middle of the Northern Baltic Proper (Fig. 1). The measurements are nearly continuous and have only few gaps, with the exception of the period when there is risk of ice. The measuring place is in water about 100 m deep, and therefore free from shallow water effects. Table 4 is the type A scatter diagram from the Northern Baltic Proper wave buoy. Only measured data is taken into account in this type of statistics, and no corrections have been made to compensate the uneven distribution of missing values. The measurements were made approximately every hour. The table gives the number of hours that given conditions have prevailed when measurements have been _ made. 18 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

16 E 20 E 24 E 28 E 66 N i 1 66 N åLr WAVE MEASUREMENTS ~s.. 1973-1999 ~s

6416.1 23 21.5 1: 64 N — Ulkokalla 1977-78,1980-81 64 N

Bothnian Sea 1997- Kylmäpihlaja 1992 Sandbäck 1973-75,1981

Helsinki 1982-85, 1990-92, 1994 Porkkala 1993 62 N — 6118\ BS — 62 N 2014£ ~7 Bogskär 1982-86 Suomen Leijona 1996-97 Northern Baltic Proper 1996- Gotland 199

/. r Aqtv 4 60 N ~ 60 N

5928.0\ C 5930.0\ 2021.0£ B n 2050.0£ NB2100.09I5.0 F. \

58 N — 58 N

G ;4:N

56 N — — 56 N

Merentutkimuslaitos Haysforskningsinstitutet Finnish Institute of Marine Research

54 N -J 54 N 16 E 20 E 24 E 28 E

Fig. 1. The locations and measuring periods of the Finnish Institute of Marine Research wave measurements.

Table 5 is the type C scatter diagram from the Northern Baltic Proper. Only the part of the year when the sea is ice-free during an average winter has ben taken into account when statistics were calculated. Missing values during the ice-free season have been estimated from the seasonal distributions. This means that the table gives the number of hours that given conditions are expected to prevail during four years. Because Table 5 gives hours rather than percentages it is identical with the type B statistics except for the two bottom lines (0 to 0.25, and the total), which will be different in type B statistics. Scatter diagram wave statistics from the northern Baltic Sea 19

Note that Tables 4 and 5 are almost identical. This reflects the small number of gaps during the ice-free period in this data. In fact the number of measured observations is greater than the total in Table 5 because measurements were taken during some mild winters also when the sea statistically is not ice- free. Table 6 shows the exceedence probabilities and hours from the Northern Baltic Proper permanent wave buoy. For convenience the probabilities are given both as percents and parts per million, as well as hours per 4 years and hours per 100 years. Table 6 is type C statistics: only the part of the year when the sea is ice-free during an average winter is taken into account in calculating the statistics. Missing values during the ice-free season have been estimated from the seasonal distributions. The numbers in Table 6 for hours per 4 years and hours per 100 years are identical to type B statistics with the exception of the bottom line. On the other hand, none of the numbers for the percents and ppms are equal to type B statistics.

Table 4. Scatter diagram of type A from Northern Baltic Proper 59°15.0 N 21°00.0 E. September 1996 — September 2000. All directions. Unit: duration in hours. In this table the middle of the range of the peak wave period Tp is shown: 2s means Tp < 2.5s, 3s means 2.5s < Tp < 3.5s etc.

Significant peak wave period [s] wave height [m] 2 3 4 5 6 7 8 9 10 11 12 Tot 7.75:8.00 0 0 0 0 0 0 0 0 0 0 0 0 7.50:7.75 0 0 0 0 0 0 0 0 0 0 0 0 7.25:7.50 0 0 0 0 0 0 0 0 0 2 0 2 7.00:7.25 0 0 0 0 0 0 0 0 0 0 2 2 6.75:7.00 0 0 0 0 0 0 0 0 0 4 3 7 6.50:6.75 0 0 0 0 0 0 0 0 I 4 1 6 6.25:6.50 0 0 0 0 0 0 0 0 4 2 0 6 6.00:6.25 0 0 0 0 0 0 0 0 7 10 2 19 5.75:6.00 0 0 0 0 0 0 0 0 9 5 0 14 5.50:5.75 0 0 0 0 0 0 0 2 11 9 2 24 5.25:5.50 0 0 0 0 0 0 0 11 17 11 0 39 5.00:5.25 0 0 0 0 0 0 I 5 16 1 2 25 4.75:5.00 0 0 0 0 0 0 0 4 10 5 0 19 4.50:4.75 0 0 0 0 0 0 10 40 41 8 1 100 4.25 : 4.50 0 0 0 0 0 1 4 29 8 4 0 46 4.00:4.25 0 0 0 0 0 1 36 58 38 7 0 140 3.75:4.00 0 0 0 0 0 9 72 84 39 9 1 214 3.50:3.75 0 0 0 0 1 12 138 121 42 4 0 318 3.25 : 3.50 0 0 0 0 2 54 197 85 21 1 0 360 3.00:3.25 0 0 0 0 5 114 212 67 21 2 0 421 2.75 : 3.00 0 0 0 0 15 147 229 47 13 1 0 452 2.50 : 2.75 0 0 0 I 104 436 344 90 7 1 1 984 2.25 : 2.50 0 0 0 4 196 413 192 42 2 1 0 850 2.00:2.25 0 0 0 32 492 686 221 43 4 0 0 1478 1.75:2.00 0 0 0 115 846 559 187 37 3 I 0 1748 1.50:1.75 0 0 6 596 1288 586 157 35 11 I 0 2680 1.25:1.50 0 0 60 1371 1175 334 95 11 I 0 0 3047 1.00:1.25 0 0 366 1579 745 163 75 16 0 0 0 2944 0.75 : 1.00 0 64 1791 2008 624 159 62 10 2 0 0 4720 0.50:0.75 1 700 2264 1100 352 111 35 4 0 0 0 4567 0.25:0.50 274 1859 1589 744 370 136 16 4 20 1 0 5013 0.00:0.25 212 419 377 291 406 113 5 5 8 1 0 1837 Total 487 3042 6453 7841 6621 4034 2288 850 356 95 15 32082 - 20 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

Table 5. Scatter diagram of type C from Northern Baltic Proper 59°15.0 N 21°00.0 E. Missing values predicted by seasonal distributions. September 1996 — September 2000. All directions. Unit: expected duration in hours. In this table the middle of the range of the peak wave period Tp is shown: 2s means Tp < 2.5s, 3s means 2.5s < Tp < 3.5s etc.

Significant peak wave period [s] wave height [m] 2 3 4 5 6 7 8 9 10 11 12 Tot

7.75:8.00 0 0 0 0 0 0 0 0 0 0 0 0 7.50:7.75 0 0 0 0 0 0 0 0 0 0 0 0 7.25:7.50 0 0 0 0 0 0 0 0 0 2 0 2 7.00:7.25 0 0 0 0 0 0 0 0 0 0 2 2 6.75:7.00 0 0 0 0 0 0 0 0 0 4 3 7 6.50:6.75 0 0 0 0 0 0 0 0 1 4 1 6 6.25:6.50 0 0 0 0 0 0 0 0 4 2 0 6 6.00:6.25 0 0 0 0 0 0 0 0 7 9 2 18 5.75:6.00 0 0 0 0 0 0 0 0 9 5 0 14 5.50:5.75 0 0 0 0 0 0 0 2 11 8 2 22 5.25:5.50 0 0 0 0 0 0 0 11 16 10 0 37 5.00:5.25 0 0 0 0 0 0 1 5 16 1 2 24 4.75:5.00 0 0 0 0 0 0 0 4 9 4 0 17 4.50:4.75 0 0 0 0 0 0 10 38 39 7 1 95 4.25:4.50 0 0 0 0 0 1 4 28 8 4 0 44 4.00:4.25 0 0 0 0 0 1 34 56 37 6 0 134 3.75 : 4.00 0 0 0 0 0 9 70 82 37 9 1 208 3.50:3.75 0 0 0 0 1 11 134 116 41 4 0 308 3.25:3.50 0 0 0 0 2 53 189 81 21 1 0 346 3.00:3.25 0 0 0 0 5 111 209 65 20 2 0 411 2.75 : 3.00 0 0 0 0 14 143 226 46 12 1 0 442 2.50 : 2.75 0 0 0 1 98 420 336 86 7 1 1 951 2.25:2.50 0 0 0 4 187 401 188 40 2 1 0 823 2.00:2.25 0 0 0 31 470 665 218 41 4 0 0 1429 1.75:2.00 0 0 0 111 808 536 182 36 3 1 0 1678 1.50 : 1.75 0 0 6 574 1234 564 154 33 10 1 0 2575 1.25: 1.50 0 0 55 1301 1129 321 93 10 1 0 0 2910 1.00:1.25 0 0 335 1496 703 156 73 14 0 0 0 2749 0.75 : 1.00 0 60 1645 1873 585 153 60 10 2 0 0 4388 0.50:0.75 1 628 2046 1002 331 105 34 4 0 0 0 4151 0.25:0.50 236 1688 1441 681 338 125 16 4 19 1 0 4548 0.00 : 0.25 187 370 325 257 351 98 5 5 8 1 0 1606 Total 424 2745 5852 7305 6255 3873 2235 818 342 87 15 29952 Scatter diagram wave statistics from the northern Baltic Sea 21

Table 6. Statistics of type C from Northern Baltic Proper 59°15.0 N 21°00.0 E. Exceedence probabilities. Missing values predicted by seasonal distributions September 1996 - September 2000. All directions.

Hs ppm hours/4 years hours/100 years 7.50 0.0 0 0 0 7.25 0.0 67 2 50 7.00 0.0 134 4 100 6.75 0.0 367 11 275 6.50 0.1 568 17 425 6.25 0.1 768 23 575 6.00 0.1 1369 41 1026 5.75 0.2 1836 55 1376 5.50 0.3 2571 77 1927 5.25 0.4 3806 114 2852 5.00 0.5 4608 138 3453 4.75 0.5 5175 155 3878 4.50 0.8 8347 250 6255 4.25 1.0 9816 294 7356 4.00 1.4 14290 428 10709 3.75 2.1 21235 636 15913 3.50 3.2 31518 944 23620 3.25 4.3 43070 1290 32277 3.00 5.7 56793 1701 42560 2.75 '7.2 711550 2143 53620 2.50 10.3 103302 3094 77415 2.25 13.1 130780 3917 98007 2.00 17.8 178492 5346 133762 1.75 23.5 234516 7024 175747 1.50 32.0 320490 9599 240175 1.25 41.8 417649 12509 312986 1.00 50.9 509432 15258 381768 0.75 65.6 655938 19646 491560 0.50 79.5 794531 23797 595422 0.25 94.6 946379 28345 709216 0.00 100.0 1000000 29951 749400

3.2 Measured wave statistics near Bogskär During the years 1983-1986 FIMR and the Swedish Meteorological and Hydrological Institute (SMHI) made joint wave measurements near Bogskär at 59°28.0' N 20°21.0' E. These measurements were made by a non-directional Waverider. The directional information is therefore based on wind direction data. SMHI has made wave measurements since 1978 on Almagrundet at 59°09' N 19°07' E (Svensson, 1983). The place is about 25 kin from the mainland, and therefore does not represent open-sea conditions in westerly and south-westerly wind directions. Comparison of wave statistics from Almagrundet and Bogskär shows that the wave climate at Almagrundet is generally much less severe than at Bogskär. Therefore the data cannot in general be used when defining the wave climate in the Northern Baltic Proper. However, during southerly winds the place seems fairly representative. On the 13th and 14 January, 1984 there was an exceptionally severe storm. The Waverider at Bogskär unfortunately failed before the peak of the storm. The significant wave height at that point was 6.7 in, 22 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

and the peak period 9.8 s. As the storm continued, H. up to 7.7 m was measured at Almagrundet. The wave height before the failure of the Waverider was lower at Almagrundet (because of the shorter fetch), and it is therefore likely that at least the same II, was encountered at Bogskär. If the highest waves that cause the measuring equipment to fail are just left out the statistics will be biassed in a dangerous way. Therefore the data from Almagrundet during this particular storm has been included. The Almagrundet data is presented in parenthesis in the tables. Parametric wave model calculations based on wind speed data from Utö and Nyhamn from the years 1961-1999 indicate that the 1984 storm event is considerably less common than the Bogskär data alone suggest. Calculations show only four such events: the January 1984 storm, another in January 1993, and two in December 1999. The 1999 cases were verified by the wave buoy measurements in the Northern Baltic Proper (section 2.3). Table 7 is the type A scatter diagram from Bogskär. The storm cases in brackets have been adjusted as described but no other corrections have been made to compensate the uneven distribution of missing values. The measurements were made every hour, and therefore the numbers state how many hours the given wave conditions have lasted. Table 8 is the type C scatter diagram from Bogskär. Only the part of the year when the sea is ice-free during an average winter is taken into account in calculating the statistics. Missing values during the ice-free season have been estimated from the seasonal distributions. The total measuring time during the years 1982-1986 is 14630 hours, or about 2 years of uninterrupted measurements. The measuring times are, however, concentrated in the autumn season, and three autumns are well covered. For this reason we have adjusted the total measuring time in Table 8 to 3 years. As mentioned above, the January 1984 storm seems to be a once-in-a-decade event that has by chance been captured by the measurements. The data from Almagrundet has been weighted as once in 10 years in the seasonally adjusted statistics. The data from Bogskär during the growth phase of that storm has been weighted as once in three years after H, exceeded 6 m. These deviations from the formal adjustment procedure are justified by parametric wave model calculations based on 39 years' wind statistics. The wave model uses the growth curves given by Kahma & Calkoen (1994). Table 9 shows the exceedence probabilities and hours from Bogskär. Table 9 is of type C statistics: only the part of the year when the sea is ice-free during an average winter is taken into account in calculating the statistics. Missing values during the ice-free season have been estimated from the seasonal distribution. The data in parenthesis are from Almagrundet and cover the period 1984-01-13 21:00 to 01-14 05:00. Measurements from the permanent wave buoy in the Northern Baltic Proper from 1996-2000 and wave model hindcasts from the year 1961-1999 have been taken into account when estimating the adjusted probability of the 1984-01-14 storm. The scatter diagram from corresponding unadjusted data is shown in Table 10. The values for hours per 3 years and hours per 100 years are identical to type B statistics with the exception of the bottom line. Scatter diagram wave statistics from the northern Baltic Sea 23

Table 7. Scatter diagram of type A from Bogskär 59°28.0 N 20°21.0 E 1982 -1986. All directions. Unit: duration in hours. In this table the middle of the range of the peak wave period Tp is shown: 2s means Tp < 2.5s, 3s means 2.5s < Tp < 3.5s etc. Data in parenthesis are from Almagrundet 1984-01-13 21:00 - 01-14 05:00. Data from the Northern Baltic Proper 1996...2000 and wave model hindcasts from the years 1961...1999 have been taken into account when estimating the adjusted probability of the 1984-01-14 storm data.

Significant peak wave period [s] wave height [m] 2 3 4 5 6 7 8 9 10 11 12 13 Tot 7.75:8.00 0 0 0 0 0 0 0 0 0 0 0 0 0 7.50:7.75 0 0 0 0 0 0 0 0 0 0 0 (1) (1) 7.25:7.50 0 0 0 0 0 0 0 0 0 0 0 0 0 7.00 : 7.25 0 0 0 0 0 0 0 0 0 (2) (1) 0 (3) 6.75:7.00 0 0 0 0 0 0 0 0 0 0 0 0 0 6.50:6.75 0 0 0 0 0 0 0 0 1 0 0 0 1 6.25:6.50 0 0 0 0 0 0 0 0 0 0 (1) 0 (1) 6.00 : 6.25 0 0 0 0 0 0 1 2 0 (2) 0 0 (5) 5.75:6.00 0 0 0 0 0 0 0 1 0 (1) 0 0 (2) 5.50:5.75 0 0 0 0 0 0 0 2 1 0 0 0 3 5.25:5.50 0 0 0 0 0 0 0 7 1 0 0 0 8 5.00:5.25 0 0 0 0 0 0 2 7 0 0 0 0 9 4.75:5.00 0 0 0 0 0 0 1 8 3 0 0 0 12 4.50:4.75 0 0 0 0 0 0 6 9 2 0 0 0 17 4.25:4.50 0 0 0 0 0 I 9 21 1 0 0 0 32 4.00 : 4.25 0 0 0 0 0 3 23 33 1 0 0 0 60 3.75 : 4.00 0 0 0 0 3 10 44 29 1 0 0 0 87 3.50 : 3.75 0 0 0 0 5 37 52 13 1 0 0 0 108 3.25 : 3.50 0 0 0 0 5 49 105 17 1 0 0 0 177 3.00:3.25 0 0 0 0 17 83 126 11 1 0 0 0 238 2.75:3.00 0 0 0 I 39 145 117 12 1 0 0 0 315 2.50 : 2.75 0 0 0 0 83 172 80 8 0 0 0 0 343 2.25 : 2.50 0 0 0 9 169 223 75 5 0 0 0 0 481 2.00:2.25 0 0 0 38 312 261 67 4 0 0 0 0 682 1.75:2.00 0 0 0 127 373 188 35 7 0 0 0 0 730 1.50 : 1.75 0 0 9 351 441 138 23 1 0 0 0 0 963 1.25 : 1.50 0 0 81 708 465 78 16 1 0 0 0 0 1349 1.00:1.25 0 2 416 852 400 68 7 6 0 0 0 0 1751 0.75 : 1.00 25 54 914 723 241 58 9 0 0 0 0 0 2024 0.50:0.75 147 443 918 559 162 50 21 5 5 0 0 0 2310 0.25:0.50 192 692 562 206 74 25 31 27 10 3 0 0 1822 0.00:0.25 185 321 224 103 65 58 73 50 11 6 0 0 1069 Total 549 1512 3124 3677 2854 1647 923 286 41 14 2 1 14630 24 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

Table 8. Scatter diagram of type C from Bogskär 59°28.0 N 20°21.0 E 1982-1986. Missing values predicted by seasonal distributions. Unit: hours per 3 years. All directions. In this table the middle of the range of the peak wave period Tp is shown: 2s means Tp < 2.5s, 3s means 2.5s < Tp < 3.5s etc. Data in parenthesis are from Almagrundet 1984-01-13 21:00 to 01-14 05:00. Data from the Northern Baltic Proper 1996...2000 and wave model hindcasts from the years 1961...1999 have been taken into account when estimating the adjusted probability of the 1984-01-14 storm data.

Significant peak wave period [s] wave height [m] 2 3 4 5 6 7 8 9 10 11 12 13 Tot 7.75:8.00 0 0 0 0 0 0 0 0 0 0 0 0 0 7.50:7.75 0 0 0 0 0 0 0 0 0 0 0 (0.3) (0.3) 7.25:7.50 0 0 0 0 0 0 0 0 0 0 0 0 0 7.00:7.25 0 0 0 0 0 0 0 0 0 (0.6) (0.3) 0 (0.9) 6.75:7.00 0 0 0 0 0 0 0 0 0 0 0 0 0 6.50:6.75 0 0 0 0 0 0 0 0 1 0 0 0 1 6.25:6.50 0 0 0 0 0 0 0 0 0 0 (0.3) 0 (0.3) 6.00 : 6.25 0 0 0 0 0 0 1 2 0 (0.6) 0 0 (3.6) 5.75 : 6.00 0 0 0 0 0 0 0 1 0 (0.3) 0 0 (1.3) 5.50:5.75 0 0 0 0 0 0 0 4 1 0 0 0 5 5.25:5.50 0 0 0 0 0 0 0 15 1 0 0 0 17 5.00:5.25 0 0 0 0 0 0 4 14 0 0 0 0 18 4.75:5.00 0 0 0 0 0 0 1 14 7 0 0 0 22 4.50:4.75 0 0 0 0 0 0 15 12 4 0 0 0 31 4.25:4.50 0 0 0 0 0 3 18 35 3 0 0 0 59 4.00:4.25 0 0 0 0 0 5 44 54 1 0 0 0 105 3.75 : 4.00 0 0 0 0 4 25 76 44 3 0 0 0 152 3.50:3.75 0 0 0 0 13 86 107 17 3 0 0 0 226 3.25 : 3.50 0 0 0 0 14 98 206 28 1 0 0 0 347 3.00 : 3.25 0 0 0 0 34 141 248 19 3 0 0 0 445 2.75 : 3.00 0 0 0 3 76 261 219 23 3 0 0 0 610 2.50:2.75 0 0 0 0 167 283 145 15 0 0 0 0 585 2.25 : 2.50 0 0 0 21 279 353 132 10 0 0 0 0 794 2.00:2.25 0 0 0 79 500 420 124 8 0 0 0 0 1132 1.75:2.00 0 0 0 222 573 298 64 17 0 0 0 0 1174 1.50: 1.75 0 0 24 582 657 214 47 3 0 0 0 0 1527 1.25:1.50 0 0 151 1128 671 119 35 3 0 0 0 0 2106 1.00:1.25 0 6 666 1290 575 103 11 17 0 0 0 0 2667 0.75:1.00 32 87 1311 1017 352 86 14 0 0 0 0 0 2899 0.50:0.75 190 668 1319 787 240 79 30 7 9 0 0 0 3329 0.25:0.50 254 998 806 314 128 43 41 38 13 4 0 0 2637 0.00:0.25 260 464 324 146 86 75 95 67 16 9 0 0 1543 Scatter diagram wave statistics from the northern Baltic Sea 25

Table 9. Statistics of type C from Bogskär 59°28.0 N 20°21.0 E 1982-1986. Exceedence probabilities. Missing values predicted by seasonal distributions. Unit: hours per 3 years. All directions. Data in parenthesis are from Almagrundet 1984-01-13 21:00 to 01-14 05:00. Northern Baltic Proper data from 1996...2000 and wave model hindcasts from the years 1961...1999 have been taken into account when estimating the adjusted probability of the 1984-01-14 storm data.

Hs % ppm hours/4 years hours/100 years 7.75 0.0 0 0 0 7.50 0.0 (13) 0 (10) 7.25 0.0 (13) 0 (10) 7.00 0.0 (53) (1) (39) 6.75 0.0 (53) (1) (39) 6.50 0.0 (98) (2) (71) 6.25 0.0 (111) (3) (81) 6.00 0.0 (272) (6) (197) 5.75 0.0 (330) (7) (239) 5.50 0.1 553 12 401 5.25 0.1 1310 29 951 5.00 0.2 2113 47 1532 4.75 0.3 3093 69 2244 4.50 0.4 4475 100 3246 4.25 0.7 7104 159 5153 4.00 1.2 11784 264 8548 3.75 1.9 18558 416 13462 3.50 2.9 28631 642 20769 3.25 4.4 44096 989 31987 3.00 6.4 63929 1434 46374 2.75 9.0 90002 2019 65287 2.50 11.7 117188 2629 85008 2.25 15.3 152576 3423 110678 2.00 20.3 203027 4555 147276 1.75 25.5 255350 5729 185231 1.50 32.3 323406 7256 234599 1.25 41.7 417268 9362 302686 1.00 53.6 536132 12029 388910 0.75 66.5 665336 14928 482634 0.50 81.4 813704 18257 590261 0.25 93.1 931231 20894 675515 0.00 100.0 1000000 22437 725400

4. EXTRAPOLATED WAVE STATISTICS

4.1 Method of extrapolation The aim is to determine the upper-limit extrapolation, defined so that it is unlikely to be exceeded when more data will be available. This extrapolation is intended to be used as a conservative estimate. The measured scatter diagrams show that at the location of the buoy in the Northern Baltic Proper the - wave period is longer at the same significant wave height than at Bogskär. In addition, the probability of high waves seems larger than at Bogskär. The upper-limit extrapolation is therefore based on the 26 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

data from Bogskär without adjusting the probability of the January 1984 storm. This distribution is presented in Table 10. To extrapolate the HS statistics we have used the Weibull distribution. The scatter diagram extrapolation is based on the fact that in extreme wave conditions the wave spectrum has in most cases a self-similar form that can be characterized by the significant wave height H, and the peak period Tr. This is because only in close to ideal conditions it is possible for the waves to grow to the maximum height allowed by the wind speed and fetch.

From the approximate self-similarity it follows that the significant slope H S /(gT p) is approximately

constant. In the literature an empirically determined limit H5 /(gT p) = 0.00776 has been reported in Buckley (1990). In the Bogskär data the mean value in the case of high waves was about 0.006. At a given H, the inverse gT p / H S was found to be approximately normally distributed.

The empirical data from Bogskär gave the following parameters of gT p / HS for the best fit: mean 173 and standard deviation 30. From these a two-dimensional Weibull-Gaussian distribution was constructed. The upper-limit extrapolation is presented in Table 11 in the same units as data in Tables 8 and 10. Comparing Table 11 with the observed data in Table 8 and Table 10 it can be seen that the extrapolation reasonably well describes the observations. It is higher than the observed data in Table 8 but does not follow the extreme values in Table 10 which stand out from the rest of the data. Table 12 gives the upper-limit extrapolation in parts per million (ppm) of the ice-free time. It is statistics of type C. Table 13 gives the same extrapolation in hours per 100 years. It is at the same time statistics of type B and C.

Table 10. Statistics of type C from Bogskär 59°28.0 N 20°21.0 E 1982-1986. Missing values predicted by seasonal distributions without taking into account model calculations. Unit: hours per 3 years. All directions. In this table the middle of the range of the peak wave period Tp is shown: 2s means Tp < 2.5s, 3s means 2.5s < Tp < 3.5s etc.

Significant peak wave period [s] wave height [m] 6 7 8 9 10 11 12 13 Tot

7.75:8.00 0 0 0 0 0 0 0 0 0 7.50 : 7.75 0 0 0 0 0 0 0 (3) (3) 7.25:7.50 0 0 0 0 0 0 0 0 0 7.00:7.25 0 0 0 0 0 (6) (3) 0 (8) 6.75:7.00 0 0 0 0 0 0 0 0 0 6.50:6.75 0 0 0 0 3 0 0 0 3 6.25:6.50 0 0 0 0 0 0 (3) 0 (3) 6.00 : 6.25 0 0 3 6 0 (6) 0 0 (14) 5.75 : 6.00 0 0 0 I 0 (3) 0 0 (4) 5.50:5.75 0 0 0 4 1 0 0 0 5 5.25:5.50 0 0 0 15 1 0 0 0 17 5.00:5.25 0 0 4 14 0 0 0 0 18 4.75:5.00 0 0 1 14 7 0 0 0 22 4.50:4.75 0 0 15 12 4 0 0 0 31 4.25:4.50 0 3 18 35 3 0 0 0 59 4.00:4.25 0 5 44 54 1 0 0 0 105 3.75:4.00 4 25 76 44 3 0 0 0 152 3.50:3.75 13 86 107 17 3 0 0 0 226 3.25 : 3.50 14 98 206 28 1 0 0 0 347 3.00:3.25 34 141 248 19 3 0 0 0 445 Scatter diagram wave statistics from the northern Baltic Sea 27

Table 11. Extrapolated scatter diagram, upper-limit extrapolation based on the data in Table 10. Unit: hours per 3 years. All directions. In this table the middle of the range of the peak wave period Tp is shown: 2s means Tp < 2.5s, 3s means 2.5s < Tp < 3.5s etc.

Significant peak wave period [s] wave height [mi 6 7 8 9 10 11 12 13 7.75:8.00 0 0 0 0 0 0 0 0 7.50:7.75 0 0 0 0 0 0 0 0 7.25:7.50 0 0 0 0 0 0 0 0 7.00:7.25 0 0 0 0 0 0 0 0 6.75:7.00 0 0 0 0 0 1 0 0 6.50:6.75 0 0 0 0 1 1 1 0 6.25:6.50 0 0 0 0 1 2 1 0 6.00:6.25 0 0 0 1 2 2 1 0 5.75:6.00 0 0 0 2 4 3 0 0 5.50:5.75 0 0 1 3 6 3 0 0 5.25:5.50 0 0 2 6 9 3 0 0 5.00:5.25 0 0 3 11 12 3 0 0 4.75:5.00 0 1 6 18 14 2 0 0 4.50:4.75 0 2 13 28 15 1 0 0 4.25:4.50 0 5 25 40 13 1 0 0 4.00:4.25 1 10 45 50 9 0 0 0 3.75:4.00 2 23 76 54 5 0 0 0 3.50:3.75 5 48 116 47 2 0 0 0 3.25:3.50 13 98 153 31 0 0 0 0 3.00:3.25 34 183 165 14 0 0 0 0 28 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

Table 12. Extrapolated scatter diagram of type C, upper-limit extrapolation. The missing values are predicted by the seasonal distribution and the data in the average ice-free period have been used. Unit: parts per million (ppm) All directions. In this table the middle of the range of the peak wave period Tp is shown: 2s means Tp < 2.5s, 3s means 2.5s < Tp < 3.5s etc.

Significant peak wave period [s] wave height [m] 6 7 8 9 10 11 12 13 14 15 9.25:9.50 0 0 0 0 0 0 0 0 0 0 9.00:9.25 0 0 0 0 0 0 0 0 0 0 8.75:9.00 0 0 0 0 0 0 1 1 0 0 8.50:8.75 0 0 0 0 0 1 1 1 0 0 8.25 : 8.50 0 0 0 0 0 1 2 2 1 0 8.00:8.25 0 0 0 0 1 2 4 2 I 0 7.75:8.00 0 0 0 0 1 4 6 3 1 0 7.50:7.75 0 0 0 1 3 8 9 4 0 0 7.25:7.50 0 0 0 1 6 13 12 4 0 0 7.00:7.25 0 0 0 3 11 21 17 4 0 0 6.75:7.00 0 0 1 5 20 34 22 4 0 0 6.50:6.75 0 0 2 11 36 52 25 3 0 0 6.25:6.50 0 0 3 21 64 76 27 2 0 0 6.00 : 6.25 0 1 7 42 109 102 27 1 0 0 5.75:6.00 0 2 16 81 178 128 23 1 0 0 5.50:5.75 0 4 33 154 276 145 17 0 0 0 5.25 : 5.50 1 8 70 282 399 147 10 0 0 0 5.00:5.25 1 18 146 496 539 129 5 0 0 0 4.75 : 5.00 3 41 298 828 635 95 2 0 0 0 4.50:4.75 7 93 594 1286 666 56 0 0 0 0 4.25:4.50 17 210 1141 1825 593 25 0 0 0 0 4.00 : 4.25 40 470 2078 2298 426 8 0 0 0 0 3.75 : 4.00 98 1038 2509 2477 232 1 0 0 0 0 3.50:3.75 245 2221 5337 2174 87 0 0 0 0 0 3.25 : 3.50 618 4507 7024 1445 19 0 0 0 0 0 3.00:3.25 1557 8402 7568 652 2 0 0 0 0 0 Scatter diagram wave statistics from the northern Baltic Sea 29

Table 13. Extrapolated scatter diagram (of type B and C). The missing values are predicted by the seasonal distribution and the data in the average ice-free period have been used. Unit: hours per 100 years. All directions. In this table the middle of the range of the peak wave period Tp is shown: 2s means Tp < 2.5s, 3s means 2.5s < Tp < 3.5s etc.

Significant peak wave period [s] wave height [m] 6 7 8 9 10 11 12 13 14 15 9.25 : 9.50 0 0 0 0 0 0 0 0 9.00: 9.25 0 0 0 0 0 0 0 0 8.75 : 9.00 0 0 0 0 0 0 1 0 8.50 : 8.75 0 0 0 0 0 0 1 0 8.25 : 8.50 0 0 0 0 0 1 2 0 8.00 : 8.25 0 0 0 0 1 2 3 0 7.75 : 8.00 0 0 0 0 1 3 4 0 7.50: 7.75 0 0 0 0 2 5 6 0 7.25 : 7.50 0 0 0 1 4 9 9 0 7.00 : 7.25 0 0 0 2 8 16 12 0 6.75 : 7.00 0 0 1 4 14 25 16 0 6.50 : 6.75 0 0 1 8 26 38 18 0 6.25 : 6.50 0 0 3 15 46 55 20 0 6.00 : 6.25 0 1 5 30 79 74 19 0 5.75 : 6.00 0 1 11 59 129 93 16 0 5.50 : 5.75 0 3 24 111 200 106 12 0 5.25: 5.50 0 6 51 205 289 107 7 0 5.00 : 5.25 1 13 106 360 384 94 4 0 4.75 : 5.00 2 30 216 600 460 69 1 0 4.50 : 4.75 5 67 431 933 483 41 0 0 4.25: 4.50 12 152 828 1324 430 18 0 0 4.00 : 4.25 29 341 1507 1667 309 6 0 0 3.75 : 4.00 71 753 2545 1797 168 1 0 0 3.50 : 3.75 178 1611 3871 1577 63 0 0 0 3.25: 3.50 448 3269 5095 1049 14 0 0 0 3.00 : 3.25 1129 6095 5490 473 2 0 0 0

5. ESTIMATED WAVE CONDITIONS IN THE ÅLAND SEA

The only part of the route between Turku and Stockholm that is exposed to open sea waves is the Åland Sea. Even there only the waves coming from SSE and possibly NNW can be large. The short fetch effectively limits the wave growth from other directions. This means that as long as the ship retains its normal course between Turku and Stockholm it will not be exposed to high waves from the bow. The following table gives estimates of the significant wave height when the probabilities of exceedence are 1 %, 3 %, 5 %, and 10 %. Only the open sea season (the time when the sea is free of ice at the point considered) is taken into account. Calculations are based on the directionally stratified statistics at Bogskär using a wave model. The effect of the Åland archipelago and the decay in shallow water areas have been taken into account in these estimates. Estimates are of type C.

30 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

Table 14. Estimates of significant wave heigth in the Åland Sea and Bogskär. Statistics of type C.

Åland Sea Bogskär

10 1.7 m 2.7 5 2.0 m 3.2 3 2.2 m 3.5 1 2.6 m 4.1 A crude estimate of rare conditions: 0.03 4.0 m 5.7

6. DURATION OF HIGH WAVE CONDITIONS

Tables 6 and 9 show that significant wave height 4 m is exceeded only about 1 % of the time or about 70 hours per year in the Northern Baltic Proper. In addition, this time is not continuous but is split into short storms. Figure 2 shows how Hs has varied in October 1998.

EUR + NBP wave buoy

5 10 15 20 25 30 October 1998

Fig. 2. Significant wave height in October 1998 at the Northern Baltic Proper permanent wave buoy. The measurements are indicated by crosses and the model hindcast by solid line.

Figures 3 and 4 show the duration of highest wave conditions in different seasons. While high waves can occur in all seasons, in practice they are restricted to autumn and winter time. Scatter diagram wave statistics from the northern Baltic Sea 31

Wave data from Station Bogskär Years 1982-1985 100 � Hs>0.5 m x Hs>1.0 m o Hs>1.5 m 80 • Hs>2.0 m

A Hs>2.5 m

A Hs>3.0 m o Hs>3.5 m • Hs>4.0 m N o Hs>4.5 m • Hs>5.0 m

~" ~-----~ 0 1 2 3 4 5 6 7 8 9 10 11 12 Month

Fig. 3. Probabilities of wave conditions in different months. Wave data from Bogskär 1982-1985.

Wave data from Station Bogskär Years 1982-1985 8

o Hs>4.0 m o Hs>5.0 m * Hs>7.0 m

0 a 1 2 3 4 5 6 7 8 9 10 11 12 Month

Fig. 4. Probabilities of highest wave conditions in different months. Wave data from Bogskär 1982-1985. 32 Kimmo K. Kahma, Heidi Pettersson & Laura Tuomi MERI No. 49, 2003

References

Buckley, W.H. 1990: The determination of ship loads and motions: A recommended engineering approach. — Naval Engineers Journal, May 1990, 209-227. Forristall, G.Z. 1978: On the statistical distribution of wave heights in a storm. — Journal of Geophysical Research 83, 2353-2358. Kahma, K.K. & Calkoen, C.J. 1994: II.8 Growth curves observations. — In: Komen, G.J. & al. (eds.), Dynamics and modelling of ocean waves. Cambridge University Press. Kahma, K.K., Pettersson, H. & Tuomi, L. 2000: Wave statistics from the Northern Baltic Sea for passenger ship bow structural design survey. — Finnish Institute of Marine Research report. — 26 pp. (mimeogr.) Svensson, J. 1983: Vågdata från svenska kustvatten 1983. — SMHI, Projectresultat Rfn/LET 1984:26. ASSESSMENT

State of the Gulf of Finland in 2002

Matti Perttilä' (Editor)

Mikaela Ahlman3, Pekka Alenius', Jan-Erik Bruun', Sergey Golubkov4, Maria Gästgifvars2, Hannu Haahtil, Kalervo Jolma2, Pentti Kangas2, Mikko Kiirikki2, Mirja Leivuoril, Vera Nikulina4, Vadim Panov4, Heikki Peltonen, Heikki Pitkänen2, Anna-Liisa Pikkarainen', Eija Rantajärvi', Lotta Ruokanen', Paula Väänänen

'Finnish Institute of Marine Research, P.O. Box 33, FIN-00931 Helsinki, Finland (FIMR) 2Finnish Environment Institute, (FEI), P.O. Box 140, FIN-00251 Helsinki, Finland 3Uusimaa Regional Environment Centre 4St. Petersburg University, Zoological Institute

The beginning of the year 2002 in the Gulf of Finland was characterized by the breakdown of the salinity stratification. During the continued stratification that ended late 2001, the oxygen depletion had weakened the populations of the bottom animals, leading also to accumulation of phosphate from the sediments into water. The breakdown of the stratification quickly improved the bottom oxygen conditions, but also transported large amounts of phosphate into the surface layer by means of vertical mixing and upwelling. Because of the high phosphate content in the surface layer, already in spring the first visions were made that the late summer's blue-green algal blooms would be intensive. During late summer, warming of the surface waters, together with occasional upwellings, further accelerated the bloom. As a result, the bloom extension and intensity corresponded those of the record year so far, 1997. There are every year 400-800 observed oil and bilge spills in the whole Baltic Sea area. The total number of spills is probably much greater. According to HELCOM, more oil enters each year in the Baltic Sea from illegal discharges than from accidents. The Finnish authorities observed 75 oil spills in 2002, which is less than in the two prior years. The problem of illegal outlets seems to continue and the number of discharges can become greater while the marine traffic in the Gulf is increasing. At present the sanctions against polluters are not effective enough. Same time the amount of transported oil through the Gulf of Finland is expected to increase from the present about 70 million tons up to 130 million tons in 2010. The concentrations of the classical organochlorines PCBs and DDTs in fish tissue, as well as those for the heavy metals lead, cadmium and mercury, remained roughly on the same level as in the previous year. In the long run, however, the slowly decreasing trend in organochlorine concentrations is evident.

Hydrography, oxygen conditions and nutrient cycling The year 2002 started in the Gulf of Finland with favourable oxygen conditions. In 2001, the whole Gulf hydrography was characterized by strong stratification, with near-bottom salinities increasing steadily until August-September, when maximum salinity, minimum oxygen (close to zero), maximum phosphate and maximum silicate concentrations were observed. The stratification was broken down towards the end of 2001 due to northerly storms. Consequently, near-bottom oxygen concentration increased, while phosphate and silicate decreased. On the other hand, as a result of the strong vertical mixing, surface water phosphate-P increased by 30 to 50 % compared with the previous winter. Due to the strong increase in phosphate, inorganic N:P ratio decreased and free bioavailable P was present in the surface layer after the spring bloom. Seasonal and annual variability make it difficult to assess long-

34 Matti Perttilä (Ed.) MERI No. 49, 2003 term trends in the surface layer data (Fig. 1). In the deep water of the Gulf of Finland, the nitrate concentration was continuing the slow decrease which had started in the early 1990's (Fig 2). The decreasing nitrate trend is perhaps a consequence of the decreasing nitrate discharges into the Gulf of Finland. Phosphate concentrations in the deep layer are closely connected to the oxygen variability and thus the increase (Fig. 2) does not reflect changes in discharges, but rather changes in the stratification.

Fig. 1. Seasonal nutrient cycling and development in the surface layer (<10m) of LL7 based on FIMR data (FIMR, H. Haahti).

—0—PO4 =I■— NO3 NH4 Linear (NO3) Linear (PO4)

Fig. 2. Nutrient development in the deep water (>75m) of LL7 based on FIMR data (FIMR, H. Haahti).

In the summer of 2002 near-bottom oxygen conditions were, in general, better than during the previous summer, especially in the northern part of the Gulf (Finnish side, Fig. 3). However, a large anoxic bottom area was found in the southern Gulf of Finland (Estonian side), extending from the westernmost Gulf to the Bay of Narva. Strong salinity stratification in the area furthered the oxygen depletion. In the deep water (>80 m) the salinity varied between 9 and 10 psu while usually the values have been 7-8 psu. Assessment — State of the Gulf of Finland in 2002 35

3.0

Fig. 3. Near-bottom overall oxygen concentrations in August 2002 in the Baltic Sea (based on FIMR and FEI data). The small anoxic/hypoxic coastal basins by the northern coast are hidden by the DAS- programme adjusted for open sea areas. (No data for the easternmost Gulf of Finland) (FEI, Mikko Kiirikki).

The strong sedimentation of organic detritus together with restricted water exchange with the open sea has led into poor oxygen conditions of the sediment-water interface of several coastal basins (Fig. 4). There were no benthic animals left in these areas characterized by reduced or only poorly oxydized sediment surfaces. However, the general the oxygen conditions of the sediment surfaces in the Gulf of Finland in 2002 were somewhat better than in 2001.

State of the sediment surface in August 2002

.oQ C vc ~ y

p ~ apv ~ ~'e e aö~ oP~w e R~' IIsp , e i o~ CY ~ U

U U Oxidized surface > 5mm U Y Oxidized surface < 5mm Reduced surface

Fig. 4. State of the sediment surface in August 2002 (data from FEI and the Finnish Regional Environment Centres) (FEI, Pentti Kangas).

The favourable hydrographic conditions in the northern side continued until summer 2002, at which period the oxygen concentrations reached its maximum in the open Gulf of Finland. In late summer the -- stratification started to strengthen again. As a consequence, oxygen content in the bottom-near layer decreased rapidly, being again close to zero in October. In December 2002, the near-bottom layer of 36 Matti Perttilä (Ed.) MERI No. 49, 2003 saline water had reached the eastern Gulf of Finland at the depth of 60 m. Oxygen concentrations were below 3 mill in the whole open Gulf. Reason to the observed inflow of saline water from the Baltic Proper may be the high atmospheric pressure which have persisted over the southern Finland almost continuously since July 2002 and caused exceptionally low water levels in the Gulf of Finland.

Intensities of cyanobacterial blooms in the Gulf of Finland were similar to the record summer 1997 The strong stratification built up during the last years in the Gulf of Finland disappeared 2001 during the autumn storms. The phosphate released during anoxic conditions from the sediment was then transported up to the surface. After the spring bloom there were still plenty of phosphate phosphorus in the surface waters. These conditions favoured the formation of extensive cyanobacterial blooms during the hot summer 2002 (Figs. 5, 6). Cyanobacterial growth accelerated towards the end of June, however strong winds kept algae mixed in the water column and cooled the surface waters. Occasional upwellings transported more nutrients up to surface in the northern coast of the Gulf of Finland. As the weather calmed and surface waters warmed up in early mid-July extensive cyanobacterial surface aggregations built up. These consisted of the non-toxic Aphanizornenon flos-aquae and toxic Nodularia spumigena and spread out to cover most of the open Gulf. By the third week in July Nodularia turned out to be the most abundant cyanobacterium, even if the total amount of cyanobacteria decreased. In the end of July strong winds mixed the surface aggregations to water column. However, while the weather became calm some large, but discontinuous accumulations were formed again in the Gulf of Finland. Aphanizomenon was clearly more abundant than Nodularia. By mid-August the extensive surface aggregations disappeared. In the end of August some local blooms were still observed in nutrient rich and sheltered bays of the Gulf of Finland. Surface water temperatures in all Finnish offshore areas were at the time several degrees higher than usual.

CYANKDBA.+s}TERIAL BLCD.CAAS 2002

VorY :I 'I I1.lo4:rn

Vi e;i bl olticaftr+:3 bloom

tie :ela p:I . r itt ll

G ~

FINNISH II\1•17: f I T L! T E UF- IVIA4-1.1lt+Jt-- Se,AF`ilCH Fr"O1'1I1er GLIO 4",` I

Fig. 5. Cyanobacterial blooms in the Gulf of Finland and the north-eastern Baltic Proper in 2002 (maximum extension) (L. Ruokanen 2003). Assessment — State of the Gulf of Finland in 2002 37

Bloom forecast for summer 2002 Observed surface blooms in July 13 and 16 Risk for surface blooms of Combination of two MODIS •; toxic nitrogen fixing satellite images showin :3 cyanobacteria surface chl-a distributiv 20 ughl very high

high

moderate

low

SYKE: Mikko Kiirikki

Fig. 6. Cyanobacterial bloom forecast and satellite observation for 2002 (data from FIMR, FEI, Regional Environment Centres).

The development of blooms in 2001-2002 resembled in many ways the development that led into the record bloom in 1997. In both years 1997 and 2002 the initial levels of surface water phosphate phosphorus in the beginning of summer were high, thus increasing the growth potential of cyanobacteria. Followed by exceptionally calm, sunny and hot summers, the successful growth of algae came out in extensive cyanobacterial surface accumulations. However in year 2002, unlike in year 1997, the algae floats kept mostly offshore from the Finnish coast and after mid-August no large surface aggregations occurred.

State of littoral zone in the Russian part of the eastern Gulf of Finland Eutrophication is a serious environmental problem for the coastal zone of the eastern part of the Gulf of Finland. It brings about an intensive development of filamentous alga Cladophora glomerata and periodical blue-green algal blooms. The biomass of Cladophora glomerata in the northern shallow littoral in the Resort District of St. Petersburg was about 100 g with maximum value of dry weight per m2 of the bottom. The biomass of Cladophora on hard substrates along the southern coasts of the eastern Gulf of Finland was even higher than at its northern coast and reached 370-530 g of dry weight per m2 of hard substratum at the depth of 1.5 m at the southern littoral near Sosnovyj Bor. On the contrary, chlorophyll a and total phosphorus concentration were considerably higher at the northern coast of the Neva Estuary. Chlorophyll a concentrations were in the range of 3.8-31.47 mg/m3 along the northern coast and of 1.47-2.94 mg/m3 along the southern coast of the Neva Estuary. Total phosphorus concentration ranged from 32 to 138 mg/m3 in the northern littoral and from 34 to 52 in the southern littoral of the eastern Gulf of Finland. Average Secchi depth along the northern coast was only 0.83 m to the contrast of 2.45 m at the opposite side of the Gulf. Massive blue-green blooms happened in shallow water along the northern coast of the Gulf of Finland from Pesochnoe to Primorsk in July - 2002. The dominant species was Anabaena flos-aquae Breb. Apparent toxic effects of blue-green blooms were also observed.

38 Matti Perttilä (Ed.) MERI No. 49, 2003

PCB and DDT decreasing, mercury and lead as usual In accordance with the COMBINE Programme of HELCOM, the concentrations of the organochlorines PCB and DDT in the Baltic Sea herring are monitored annually since early 1980's at five catch areas in the northern Baltic Sea. 2-years old female herring have been used for the monitoring of the contaminant concentrations. The organochlorine time series (Fig. 7) shows the development of the total PCB concentration (sum of 7 main representative PCB components), as well as that of the total DDT concentration (DDT plus the metabolites DDE and DDD) in the Baltic Sea herring muscle. In the long run, the concentrations are obviously slowly decreasing, while large annual variations are seen.

20 16 16 14 € 12 9 10 x.1985-1999: results in tissue homogenate v. 2001: average of results in individual tissue samples

Fig. 7. Concentrations of the total PCB and total DDT in the 2-year Baltic Sea herring muscle (µg/kg fresh weight) (FIMR, Anna-Liisa Pikkarainen and Hannu Haahti).

Trace element (cadmium, copper, mercury, lead, zinc) concentrations in the Baltic Sea herring muscle have been monitored since 1979 in herring at the same areas. Mercury concentrations on the average are slightly lower in the Gulf of Finland than in the Gulf of Bothnia or Åland Sea. The concentrations are stabilizing after a sudden rise in mid 1990's (Fig. 8). In older herrings, both the organochlorines and the mercury concentrations have been at a considerable higher level (two- even fivefold) compared to the 2-year old individuals used for the regular monitoring. This repeats the earlier observations and indicates an almost linear accumulation of the lipid-soluble contaminants (organochlorines and methylated mercury) with age. However, the observed contaminant concentrations are only 2-10% of the concentrations allowed for fish for human consumption. Assessment - State of the Gulf of Finland in 2002 39

00e0 0070 0.060 0.090 0.000 0.060 0.020 0010 0.000 # $ 1,3 P

3.030 0.070 0.060 0.050 0.040 0.030 0_020 0.010 0.000 0.030 Y/ å $ ~ . ~ k f k k, 0.070 0.060 0.000 0.050 0.Ji0 0.040 0.191 O.BGO 3.030 <.u 0 0.030 0.020 L If 0.040 0.070 /./3 0 0.030 0.000 B 0.1u/ 0.000 å N E k E N 0.010 1.11 0 lllllll~~~~ 0.000

0.110 1Ji0 6äääåååd8å ås

Fig. 8. Development of the mercury concentrations in the 2-year Baltic Sea herring muscle (mg/kg fresh weight) (FIMR, Mirja Leivuori).

Increasing oil transport constitutes a serious threat There are every year 400-800 observed oil and bilge spills in the whole Baltic Sea area. The total number of spills is probably much greater because all Baltic Sea countries do not have equipment for aerial observation and aerial surveillance cannot be conducted 24h/day. According to HELCOM, more oil enters each year the Baltic Sea from illegal discharges than from accidents. Most of the illegal oil spills are observed in the international waters of the Gulf of Finland (Fig. 9). The Finnish authorities observed 75 oil spills in 2002, which is 32 spills less than in the previous year. Estonian authorities reported 3 spills in 2002. In addition to the large number of illegal oil spills, the increasing oil transport through the Gulf of Finland forms a serious threat to the marine and coastal environment (Fig. 10). In 2002, approximately 70 million tons of crude oil was transported through the Gulf. It has been estimated that the amount of transported oil will be more than 130 million tons by 2010 In combination with the already dense ship traffic across the Gulf between Helsinki and Tallinn, the increasing oil transport means increased collision risk. The debate on oil transportation has been intensive mainly due to the newly opened Primorsk oil harbour and the severe ice conditions in the Gulf of Finland. Late in the year, tankers not in accordance with the strictest Finnish ice classification rules were used for oil transportation from the Russian harbours. These issues have raised a public concern about threats related to oil transportation in the Gulf of Finland. A unified Russian, Finnish, Estonian information system (VTMIS) for the control of movements of ships in the Gulf of Finland is under development. This service will enhance the maritime safety as it will come into operation in summer 2004. HELCOM is investigating the feasibility of designating the - Baltic Sea or parts of it as a particular sensitive sea area (PSSA). PSSA would promote maritime safety by fostering environmental awareness, for instance by registration of the area in sea charts. The experts

40 Matti Perttilä (Ed.) MERI No. 49, 2003

agree on the need to unify the rules for Baltic ice classification of ships. IMO has been preparing guidelines for ships operating in arctic ice-covered waters but currently no international regulations are in place to demand special construction features of a ship sailing in icy waters. The accident cases, the Prestige (November 2002) and the Erika (December 1999), have accelerated preparation on EU legislation in the field of prevention of marine pollution. In December 2002 were given proposals for regulations to speed up the phasing-out of single hull oil tankers and ban the use of single hull tankers in transportation of most persistent oil in EU. From the beginning of 2004 the Baltic Sea will be almost entirely the inland sea of the European Union and EU regulations should be implemented in the area.

, > ~y~ 6 (~ O$6Sv F at4e ,

y _~1~d~ ~~~ ~i p ♦ {

P ~ =~ ® 0 — ® —

o ® --- ® 0 ~eP ~ _ ® ~ ~ ~~~ ®

OD 0

0 6 ® 6 ®

Fig. 9. Observed oil spills in 2002 in the Gulf of Finland (FEI 2003, Heli Haapasaari).

Assessment — State of the Gulf of Finland in 2002 41

120

OTallinn

100 - ❑ Vysotsk

Batareynaja s ❑

nne 80 - ❑ St.Petersburg to ❑ Primorsk lion il

M 60 - ❑ Porvoo

40 -

20 -

0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Fig. 10. Oil transportation in the Gulf of Finland through main oil ports in years 1995-2002 and estimated development 2003-2005 (FEI 2003, Kalervo Jolma). 42 SHORT-TERM EFFECTS OF NUTRIENT REDUCTIONS IN THE NORTH SEA AND THE BALTIC SEA AS SEEN BY AN ENSEMBLE OF NUMERICAL MODELS

Tapani Stipal, Morten Skogen2, Ian Sehested Hansen3, Anders Eriksen3, Inga Hensel, Anniina Kiiltomäkil Henrik Soiland2 & Antti Westerlund'

' Finnish Institute of Marine Research, FIN-00931 Helsinki

2 Institute of Marine Research, Postboks 1870 Nordnes, N-5817 Bergen 3 DHI Water and Environment, DK-2970 Horsholm, Denmark

EXECUTIVE SUMMARY

The NO COMMENTS project of the Nordic Council of Ministers (NMR) has pursued to demonstrate that three-dimensional numerical biogeophysical ocean modeling in Nordic sea areas (North Sea, Baltic Sea and their transition area) can provide information and evidence needed for decisions on what constitutes dangerous anthropogenic interference with the marine ecosystems (after IPCC, Synthesis Report 2001, mutatis mutandis). The results now call for a tighter coupling to the decision-making process as well as a continuing transparent quality assurance. Three different three-dimensional coupled physical-biological ocean models have been applied to study the effects of reduced external nutrient input to the on the development of primary producers. In line with the reductions proposed by e.g. HELCOM and OSPARCOM, scenarios with 30 % reductions in dissolved inorganic nutrient input have been carried out. The experiments have been focused on the short-term effects induced by nutrient input reductions, i.e. those changes that are visible within one year. The reduction effects are studied in terms of four environmental quality indicators: the nitrogen to phosphorus ratio and the vertically integrated phytoplankton biomass in the middle of the summer, the annual maximum of the phytoplankton biomass, and the total annual primary production. The effects of the nutrient reductions on this set of indicators are found to differ between the modeled areas but also between the models. The largest sensitivities are found on the west coast of Denmark, the Kattegat area as well as in the Gulf of Riga and the Bay of Bothnia in the Baltic Sea. The effect of the reduction is seen to depend on the initial state of the sea area. In general, the reduction effects are relatively small on the modeled time scale, and in many cases constrained into the immediate vicinity of the source. The NO COMMENTS scenario study has shown the feasibility of the three-dimensional coupled physical-biological ecosystem modeling approach in evaluating the effects of managerial decisions in the NMR area. As with models describing the transport and reactions of atmospheric pollutants, several lines of improvement remain after this exploratory phase. Future directions for the work should include the improvement and continuous validation of the models with a special emphasis on long-term changes of the ecosystem (e.g. improved parameterisation of small-scale processes and regeneration), the derivation of environmental indicators of improved sophistication for hindcast, nowcast and forecast purposes, as well as a tighter coupling to decision making models and processes possibly along the lines already followed in the atmospheric pollution studies. Key words: eutrophication, ensemble, numerical models, nutrient runoff, atmospheric deposition, integrated assessment 44 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Solland & Westerlund MERI No. 49, 2003

1. BACKGROUND

1.1 Modeling of eutrophication in the NMR area

The supply of nutrients and the possible eutrophication of the open sea and coastal waters (e.g. extensive algal blooms, oxygen depletion in bottom waters, extinction of bottom living species) is of great interest both to management and scientist. Therefore, to provide scientific background information on eutrophication and the marine environment for use by management in the different Nordic countries, several projects supported by the Nordic Council of Ministers (NMR) have been related to eutrophication. Taking into consideration the complexity of the marine ecosystems, including both processes and variability in time and space, it is believed that not only in situ data, but also coupled 3 dimensional biogeochemical ocean models, should be part of a system for making an integrated assessment of environmental information available. Such models will be an important tool for planning and decision, e.g. within:

• environmental status in different areas • assessment of the spatial influence of polluting activities • calculating source apportionment from different areas • forecasts of pollutants (spatial and temporal) when the load is changed (floods, reduction scenarios) • improved basis for causal relationships, e.g. algae blooms and oxygen deficiencies • monitoring and forecasting of the environmental now situation

The NMR project conducted by Aksnes & Selvik (1998) concluded Clearly, there exists a potential for taking such models (numerical models) into environmental management. We conclude that an appropriate designed model system will enable a sound and efficient environmental management of large marine systems like the North Sea. Compared to actual costs of nutrient reduction and monitoring programs, a modeling approach is inexpensive and once formulated it serves as an objective method. The main objection against a modeling approach is the apparently high diversity of existing models. Obviously, a numerical model, being part of a management system, needs accept from the international scientific community. This requires establishment of an international program where the objective should be to design and implement a model system that can optimize abatement strategies for eutrophication in the marine environment. Through NO COMMENTS the main groups doing biogeochemical modeling in the ocean have worked together with the common goal: To develop an operational modeling tool for environmental management and planning in the Baltic Sea and the North Sea. In the present study three of these state- of-the-art ecological models running at DHI Water and Environment (DHI), Finnish Institute of Marine Research (FIMR) and Institute of Marine Research, Norway (IMR) have been used in a common setting to investigate the effect of a possible management decision on reduced nutrient inputs to limit the risk of marine eutrophication. The effect of such an initiative, which e.g. can be a basis for negotiations within regional marine conventions, can only be investigated through a set of such model scenario simulations. Previous applications of such a modelling approach are given e.g. by Neumann & al. (2002) and Kiirikki & al. (2001). Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 45

Four different simulations (one reference and three reduction scenarios) have been defined and run by all models. The main focus have been on phytoplankton biomass, primary production, N:P ratio and nutrient fluxes, and the effect of these variables have been investigated for the different models and nutrient reduction scenarios.

1.2 Scenario definitions

All partners have analysed the consequences of the following scenarios: 0 reference situation; "no change" scenario RN 30 % reduction in dissolved inorganic nitrogen from river runoff RP 30 % reduction in dissolved inorganic phosphorus from river runoff AN 30 % reduction in atmospheric deposition of nitrogen. The aim of the scenario analysis was to concentrate on the short-term effects of nutrient reductions. Year 2000 was chosen as the as the year of the scenario analysis, since knowledge of the environmental status during 2000 was already analyses in the environmental status reports of some partners. The scenario analysis was planned to have two stages: first, the ecosystem status was spun up from initial conditions by running the models with the atmospheric and boundary development of 2000 one or two times (i.e., twice the same year). A subsequent simulation year with the same forcing then formed the actual analysis year. The model-dependent features of the scenarios and their analysis is described in what follows.

2. DESCRIPTION OF MODELS AND THEIR FORCING

All models that participated in the scenario analysis were 3-dimensional primitive equation ocean models, that describe the evolution of the ocean state in small temporal steps, typically on the order of 10 minutes. This method ensures, that the state develops in accordance with the laws of physics. The ecosystem models are embedded as reactive tracers in the physical models. The modeled tracers typically consist of inorganic nutrients, living phytoplankton biomass described as different functional groups and possibly zooplankton, detritus and oxygen. In their details, in particular the ecosystem models differ from each other and are briefly described below.

2.1 IMR

The model is the same as that used by Skogen & al. (2002). The description below is quoted from their work: 46 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Solland & Westerlund MERI No. 49, 2003

Fig. 1. The IMR model bathymetry. Coarse North Sea model domain (left) and fine Skagerrak model domain (right).

2.1.1 The model design The NORWegian ECOlogical Model system (NORWECOM) is a coupled physical, chemical, biological model system (Aksnes & al. 1995, Skogen & al. 1995, Skogen & Soiland 1998) applied to study primary production, nutrient budgets and dispersion of particles such as fish larvae and pollution. In the present study a nested version of the model is used, with a coarse 20 x 20 km grid on an extended North Sea, and a fine 4 x 4 km mesh in the Kattegat/Skagerrak area (see Figure 1). The coarse model was run initially, providing the necessary boundary and initial values for the fine grid. In the vertical 12 bottom-following sigma layers are used. The physical model is based on the primitive equation, wind and density driven Princeton Ocean Model (Blumberg & Mellor 1987). The forcing variables are six-hourly hindcast atmospheric pressure fields and wind stress from the European Center for Medium-Range Weather Forecasts (ECMWF), four tidal constituents at the lateral boundaries and freshwater runoff. The chemical-biological model is coupled to the physical model through the subsurface light, the hydrography and the horizontal and the vertical movement of the water masses. The prognostic variables are dissolved inorganic nitrogen (DIN), phosphorus (PHO) and silicate (SI), two different types of phytoplankton (diatoms and flagellates), detritus (dead organic matter), diatom skeletals (biogenic silica), inorganic suspended particulate matter (ISPM), oxygen and light. The incident irradiation is modeled using a formulation based on Skartveit & Olseth (1986, 1987), with surface solar radiation data from ECMWF as input data. Nutrients (dissolved inorganic nitrogen, phosphorus and silicate) are added to the system from the rivers, from the atmosphere (only inorganic nitrogen) and through the open boundary. Particulate matter has a sinking speed relative to the water and may Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 47 accumulate on the bottom if the bottom stress is below a certain threshold value and likewise resuspension takes place if the bottom stress is above a limit. Input data on suspended particulate matter, are taken from Pohlmann & Puls (1994). Regeneration of organic particulate matter takes place both in the water column and in the sediments. The bottom stress is due to both currents (including tides) and surface waves. Surface heat fluxes (short and long wave radiation, sensible and latent heat fluxes), are calculated using data available from the ECMWF archive. Initial values for velocities, water elevation, temperature and salinity in the coarse model are taken from monthly climatologies (Martinsen & al. 1992). Interpolation between monthly fields are used at all open boundaries, except at the inflow from the Baltic where the volume fluxes have been calculated from the modeled water elevation in the Kattegat and the climatological mean fresh water runoff to the Baltic, using an algorithm from Stigebrandt (1980). To absorb inconsistencies between the forced boundary conditions and the model results, a 7 grid cell "Flow Relaxation Scheme" (FRS) zone (Martinsen & Engedahl 1987) is used around the open boundaries. To calculate the wave component of the bottom stress, data from DNMI's operational wave model, WINCH (SWAMP-Group 1985, Reistad & al. 1988) are used. The initial nutrient fields are derived from data obtained from ICES together with some small initial amounts of algae.

2.1.2 Experimental set-up

The 20 km North Sea model was spun up by running 1998 three times and then 1998 through 2000 was run sequentially. The fine grid model was initialized with results from the coarse grid January 5, 1999. The 4 km model was run from January 5, 1999, through 2000 with boundary conditions from the 20 km North Sea model.

2.2 DHI

The 3 dimensional model used by DHI is the software package MIKE3, developed by DHI. This model is a modular based software, including among others the hydrodynamics, advection/dispersion and eutrophication modules. The model is based on the full 3D momentum equations, the continuity equation of water and the state variables and the state of water equation, and applies a finite difference scheme, where the model domain is divided into cubes of predefined length, width and thickness dimensions. For further information on the modeling software is referred to DHI (2001b,a).

2.2.1 Model Set-up

The established model set-up within MIKE3 covers the whole Baltic Sea, the transition area and the North Sea, with an open boundary to the north at the section between Stavanger and Scotland (latitude appr. 59°) and another open boundary to the south-west in the English Channel (latitude appr. 51°). 48 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Soiland & Westerlund MERI No. 49, 2003

1600 - ~ ( - p 1500 1.-- ~N I -_ + __ --- I 1400 - ?

1300 ~! j~

1200 _ ( +ll 1100- I w~° UN ---- + 1000 - I ------_! ~

700

600

500 -

400 -

300 - n i . Iy i I i1 200 - ~i 52°1,0' N li = w , 11.. ~ w - -- w 1, tu l 100 = -- ~ -- — --t ~ ~ o . 1S ö tii ~.' , c,~ I b! 4~~ b i f Lv co I I C1 0 iiliii 5 ( ii ii fi l ii t11111-111Ii iii ~..ii...i i i i i 200 400 600 800 1000 1200 1400 1600 1800 (kilometer)

Fig. 2. The nested MIKE 3 set-up with the outer 9 nm and the inner 3 nm resolution in a sub-domain covering the eastern part of the North Sea, Skagerrak, Kattegat, the Belt Sea, the Arkona and Bornholm Basins.

The basic horizontal resolution is 9 nm (nautical miles) or 16667 m. However, in the transition area between the Baltic Sea and the North Sea is applied a nesting option with a finer horizontal resolution of 3 nm (5556 m), see Figure 2. The bathymetry of the modelling domain is based on available surveys. However, some adjustments have been made in the narrow Danish Straits to ensure an appropriate representation of the deeper trenches, which are very important for the formation of the stratification in the Belt Sea area. The vertical resolution in the model is 2 m between level -6 and -80 m. Below level -80 in is applied a bottom fitted approximation with the remaining part of the local water column. The surface layer varies with the free surface elevation and extends down to level -6 m (or the local depths at shallower positions). The model consists of a hydrodynamic module, which calculates the flow velocities, surface elevations, salinities and water temperatures as well as dispersion properties. The other applied module, the eutrophication module, represents the nutrient cycling, the primary production and mineralisation and the effect on the dissolved oxygen conditions. The model run covers a whole year and runs with a temporal resolution of 300 s. The model results are generally saved with 1-day time steps for the 2D and 3D fields. Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 49

2.3 FIMR

The ecosystem model at FIMR is a coupled physical, chemical and ecological 3-dimensional ocean model. It is composed of separate modules such that the chemical-ecological model is embedded into the physical model equations as reactive, dynamically passive tracers, which are advected and diffused in each time step of the physical model. Both models are integrated numerically simultaneously. Due to the limited amount of initial resources available in this work, the model realisation represents a first step in the direction of models covering the whole Baltic ecosystem.

Table 1. DHI model boundary conditions. The applied climatological data is in general provided as monthly means.

HYDRODYNAMIC PART Open marine boundaries Actual tides (hourly values) Climatological values of temperature and salinity distribution in sections Run-off Actual values of flow for Danish, Swedish and Norwegian rivers to Skagerrak, Kattegat and the Belt Sea (monthly values) Climatological values for remaining rivers Air-sea exchange Climatological values of net precipitation (monthly values Actual wind and air pressure fields (3 hourly fields) Climatological clearness information

EUTROPHICATION PART Open marine boundaries Climatological values of nutrient concentration and DO in section Land load Actual values for Danish Swedish and Norwegian sources to Skagerrak, Kattegat and the Belt Sea (monthly values) Climatological values for remaining rivers Air-sea exchange Actual values of nutrient deposition (monthly values) Actual bioactive light information (daily mean values)

2.3.1 Physical model

The physical model at FIMR is based on the MIT GCM (Marshall & al. 1997). The model is realised for the Baltic Sea on a spherical polar grid using the bathymetric data of Seifert & Kayser (1995) (cf. Fig. 3). The spatial discretisation is made with a minimum filter for bottom topography at 6 nautical mile intervals, with 21 layers in the vertical. The south-western corner of the grid is located at 53.85° N, 8.7° E, with 120 grid cells in the latitudinal and 108 grid cells in the longitudinal direction. 50 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Soiland & Westerlund MERI No. 49, 2003

-5

-10

-15

-20

-25

-30

-35

-40

-45

-50

-60

-70

-80

-90

-100

-125

-150

-175

-200

-400

-800

10°E 14°E 18°E 22°E 26°E 30°E LONGITUDE depth regridded min

Fig. 3. Bathymetry and model area used in the FIMR model.

The vertical resolution is concentrated to the euphotic zone. The topmost layer thickness is 3 meters, but is reduced to 2 meters in the minimum for cells hugging the coast. Four subsequent layers in the vertical have a thickness of 2 meters, with an increase through 3.3 meters (3 layers) to 6 meters (10 layers). The numerical algorithms that can be applied to the study of the Baltic are numerous; here, a linearised, implicit free surface is used in the model integrations and checked for volume conservation. We opt for a non-linear flux-limited advection scheme for all tracers to retain the positive definiteness of the ecological variables. The sub-grid scale turbulence is represented in the model with the non-local KPP closure (Large & al. 1994) in the vertical. Due to non-linear stability constraints, a horizontal harmonic as well as biharmonic diffusivity is applied (k2 = 50 mes 1, k4 = 10" mos r). The horizontal turbulent Prandtl number is set to 4 to ensure numerical stability.

1.3.2 Ecological model Several ecosystem models of varying complexity are being developed at FIMR. For the present purpose, the ecosystem dynamics are described with a simple yet robust model based on a combination of the formalisms employed by Aksnes & al. (1995) (which forms the basis of NORWECOM) and Tyrrell (1999). The model (FINECO-1) consists of three conceptual phytoplankton groups (diatoms, flagellates and cyanobacteria), with a constant mortality factor, and three nutrients (phosphate, dissolved inorganic nitrogen and silicate). Diatoms are potentially limited by the availability of silicate, whereas cyanobacteria can utilize (fix) molecular nitrogen and therefore are not limited by the availability of dissolved inorganic nitrate. "Flagellates" are a functional group that mimics the autotrophic flagellates. The ecological model describes in essence the new production in the presence of three functional algal groups, with a simple parameterisation for the regeneration. Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 51

The equations in the ecological model are

aad =cd ~d —ed — nrdcd ) (1)

acf =c f (~ f — e f —rnfcf) at

aCc = cc Gic — ec — inc) at

cAT = cd t~d — ed )—cf ~ "f —ef )—cc (— ec ) aat

acp — cf(µf — ef)— ectt'c — ec)) at j'PN \ cd Old — ed)

acs at = ySN ~— cd — ed)),

where cd, cr and ct are the biomasses of diatoms, flagellates and cyanobacteria, respectively. Furthermore, cN, cp and cs are the concentrations of nitrogen, phosphate and silicate, which are used by phytoplankton in the constant ratios rpN and rsN. Note that we use a quadratic mortality rnd,f,, as opposed to the linear mortality used by Aksnes & al. (1995). The specific rate of exudation ed f c =e0a2T depends exponentially on temperature T. The phytoplankton growth rates µdisc depend on nutrient concentrations, irradiation and temperature in the following way:

µ f max = /l fOadT (7)

1/2 I / c p c~r µ f max (8) I+k~ cp + k fp c~, +k~r

where I= I(z) is the illumination at depth, µfo is the maximum growth rate at 0°C. k1p and k/N = ,ufmaa/afN are the half saturation functions as used by Aksnes & al. (1995) with constant but species- and limitation-dependent affinities a. I(z) depends on the amount of biomass between the depth z and the surface (self-shading). The limitation from multiple nutrient sources is not the minimum function of Liebig, but is based on a geometric mean of the individual limitation functions, which is a differentiable function.

2.3.3 Initial conditions Both physical and chemical initial conditions for the model integrations were obtained from winter monitoring data obtained by FIMR within the HELCOM COMBINE program in the period December 1999-February 2000. The observed values were interpolated in three dimensions with a robust nearest - neighbor interpolation. These observed initial fields for the Baltic Sea were supplemented by climatological values for the North Sea from the World Ocean Atlas (http://www.nodc.noaa.gov). 52 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Selland & Westerlund MERI No. 49, 2003

The interpolated fields were used as initial conditions for the year 2000 integration. An investigation of the model results showed, that the initial distribution of tracers is advected and diffused by the model in about 1 month (i.e., before the onset of the growth season) into smooth large-scale fields that were well suited for the ecological model. No separate spinup is done for the model.

2.3.4 Boundary conditions The physical atmospheric forcing is taken from the NCEP reanalysis data set (Kalnay & al. 1996). Surface fluxes of momentum, net precipitation, net shortwave radiation and a sum of other heat fluxes were separated as driving forces for the model. The freshwater flux from rivers in the Baltic catchment area for the year 2000 as seen by HBV model running at SMHI was kindly provided by Eleonor Marmefelt. The chemical boundary conditions were collected from the MARE database (http://www.mare.su.se/). Since the database does not contain measurements of runoff for the year 2000, the nutrient runoff is reconstructed using the HBV freshwater runoff and climatological nutrient runoff concentrations. Additionally, 20 most important point sources of nutrients were added from the HELCOM Pollution Load Compilation 3. A mean annual atmospheric deposition of inorganic nitrogen is sampled and interpolated from MARE data. The increase in mean sea surface height in the model domain due to river runoff and precipitation was validated. An open boundary with a sponge layer in the North Sea was investigated, but was not properly validated. Therefore, for the present purposes no open boundary is used, but the initial conditions and their development in the North Sea provided the required boundary conditions for the Baltic Sea through the Danish Straits. The nutrient reduction scenarios are implemented as separate forcing files with reduced nutrient fluxes for each scenario. The initial condition for all scenarios was the same.

3. Reference situation and description of indicators The reference situation describes the environmental state of the North Sea, the transition area as well as the Baltic Sea as seen by the numerical models. Since this case also acts as a "no change" scenario with no reductions in emissions, it is nicknamed "scenario 0". The state of the marine environment in these areas is depicted in terms of four environmental indicators: the inorganic N/P ratio during the summer season provides information of whether nitrogen or phosphorus is more limiting to the growth of primary producers and was chosen as an indicator because of its simplicity. A more accurate indicator of nutrient limitation in the actual model has been used in the NO COMMENTS project by Hansen & Eriksen (2002), but further indicator development was found necessary before it can be applied to multi-species models such as NORWECOM and FINECO-1. The N/P ratio has been found to have predictive value for harmful algal blooms (Kononen & al. 1996, Stipa 2002a, Graneli & al. 1999). mean summer biomass per horizontal area in July-August gives an indication of the distracting effects of pelagic algal biomass during the peak holiday season. maximum annual biomass per horizontal area indicates the areas of largest effects of biomass-related negative eutrophication phenomena (e.g. water transparency, potential for oxygen deficiency). mean annual primary production' depicts areas of highest mean carbon assimilation and therefore, the highest potential for mean annual sedimentation. Hence, it is important for the trophic level and benthic status.

1 This indicator was not analysed for FINECO-1 results due to resource and time limitations

Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 53

600 J 4

550 A 8°0' ~ ~) B 500 G-2--- ~ a 450 se, /-1 400 s~~ ~~~~R ,-/// ~ INIIP ratio [molar] Above 200 ~ 350 I a~ ,~ k. +, ~ 50 - 200 ,i • II o ~ ( 30 - 50 ö 300 5~ V l~ 24 - 30 P~ O •~gO~ • iiii r~ 20 - 24 250 7 ` 18- 20 v ~ id : ~ ~~ '~~ — 16- 18 - OVER 200 200 14 - 16 - 50-200 A 12- 14 - 60-5° - i~ ~ - 24- 30 150 10- 12 Q 20- 24 ~'~►~ 8- 10 ~ 1a. 20 ~ = ;6- 1 100 ~~~ ., . l 6- 8 = 4 - 1 W . 4- 6 = 12- 1 ~Yl ~ C 10- h 2- 4 50 MI. O a- ~`~~~ - ~ °O ~ = 6- 0- 2 o <- ~n' I. Below 0 0 2. 0 0 6- UNDER 0 100 200 300 400 - (kilometer)

5000 200 C SO

1,20

Fig. 4. Scenario 0: Time mean of surface (0-10 m) inorganic molar N/P ratio in the reference runs for 2000-07-01...2000-08-31: (a) from NORWECOM (b) from MIKE3 (c) from FIMR. The plotted quantity is

defined as/n, where FN1= JT [N]dt is the time mean of the concentration [N] in the period T. T

4. RESULTS FROM REDUCTION SCENARIOS

4.1 Scenario RN: Reduction of nitrogen from river runoff

The inorganic nitrogen from river runoff was reduced by 30 % for all rivers, and the consequences of this scenario were evaluated by running the models for one year. The results are depicted in Figures 8- 11 as a difference to the reference run.

54 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Sailand & Westerlund MERI No. 49, 2003

600

3. 550 B A ,--- Z1 500 ° — D0 , /~ - ~I,} 450 ~ ; -rni1 `-%-~ a V `T %E 1, yl 400 r, ..- Go / .R3l`f, :,~''J y% c 350 ~ .. , ~ ", E• 300 -5Å° 0^, t.}) . ` - .111a Mean of Depth int. PC [gC/m2] - Above 30 ro. r 250 .d '~`~ ~ ,c 20 - 30 - 10- 20 200 \i~ iF 8- 10 OVER 200 6- 8 - ~~ t.',. - 100-20.0 1 p / tl 4- 6 ~ o- lot 150 ~`,j' 3.5 - 4 0.0- ao 1vr, 1,~. - 1 3 - 3.5 l= 4.0- 60 ~J 2.5 - 3 IC 35- 4.0 100 ~ A _]%1v . ~ :-~ Q 3.0- 3,5 (p _g 6 2 - 2.5 ® 25- 3.0 5'l-4''a •- 1.5- 2 50 ~= \i'''1, _ • ,.• 1 -1.5 MI 1.5- 2.0 il~ 1 - 0.5- 1 IC 1.0- 15 Below 0.5 0 05- 1.0 0 I= oo- os uNDER 0.0 0 100 200 300 400 - (kilometer)

20 C 64.0°N - 10

62.0°N -

5 60.0°N - I-1 ‘•

58.0°N - 1 —

~ _7 -' r 56.0°N -

• ~ 54. 41111111— 500 i8°E 22°E 26°E 30 E

Fig. 5. Scenario 0: Time mean vertical integral of phytoplankton biomass (gm-2 of carbon) for the period 2000-07-01...2000-08-31: (a) from NORWECOM (b) from MIKES (c) from FIMR. The plotted quantity is defined as JT J°H [b,dzdt with b the biomass.

Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 55

600

A 550 B

500

450

400

350 • ~ ~ ö 300 Max of Depth Int. PC [gC/m2[ [ 1 Above 30 250 1-1 20 - 30 I I 10 - 20 I I 8- 10 200 6- 8 - OVER 20.0 4- 6 - 10.0-200 3.5 - 4 - 6o -mo 150 - 60- 60 3 - 3.5 40- 60 2.5- 3 a5 ao 100 2-2.5 30- 35 1.5- 2 25- 3.0 I 1 ~ 20- 2.5 50 0 1-1.5 ] 15- 20 El 0.5- 1 ~ 10- 1s - Below 0.5 =05- 1.0 0 0 0-0 05 0 100 200 300 400 - UNDER 00 (kilometer)

54.0`N — 10

62.0°N — ~

60.0°N — 3

58.0°N —

56.0°N o 54.0°N — 1 1,4 114 22`E 26°E 30 E

Fig. 6. Scenario 0: Maximum of the vertical integral of phytoplankton biomass (gm-2 of carbon) for the year 2000: (a) from NORWECOM (b) from MIKE3 (c) from Fl MR. The plotted quantity is defined as

max moo J9H [b]c z with b the biomass.

56 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Solland & Westerlund MERI No. 49, 2003

600

550 B

500

450

400

350 0) v E 300 • 250iI .I.I1.♦4~ Prim. Prod. IgC/m2/y) ~~~ ~ - Above 300 JI . 200 - 200 - 300 180 - 200 ~ ._t:,. 1~~~~, 160-180 ~ OVER Goo 150 ,,^60 J' -~ ) .%4/0 140-160 200- 300 1,AG N-M160-200 ~; ___ ~ ~ 120-140 0 160-180 100 100-120 40-1eo r v. , 80 - 100 120-140 ~>~ 60- 80 1=] 100-120 50 ~ 60-100 I-I 40 - 60 ~ 60. 80 ▪ 20 - 40 ~ 40- 60 '~ia. _ ~~~- ▪ Below 20 20- 40 0 - UNDER 20 0 100 200 300 400 (kilometer)

Fig. 7. Scenario 0: Total annual depth-integrated primary production (gm-2y1 of carbon) for the year 2000: (a) from NORWECOM (b) from MIKE3.

Diff. IN/IP ratio [molar] EN OVER 200 w sw IIIIIII~ Above 2 - 30- 50 N 1- 2 - 20- 30 0.5- 1 O 15- 20 I I U 10- ss ( I 0 - 0.5 O 5. 10 -0.5 - 0 I] 0- I I I I -1 --0.5 I I -2- -1 -2 •O -2U -15 r i -4- I♦ Below -4

- -~- -~ - UNDER -200 0 100 200 300 400 (kilometer)

1 00 3m C 50

s 20 ,15

,10

, ,

,-15

-50

Fig. 8. Scenario RN: The reduction (scenario RN — reference) of the time mean of surface (0-10 m) inorganic molar N/P ratio (cf. Fig. 4. For 2000-07-01...2000-08-31: (a) from NORWECOM (b) from MIKE3 (c) from FIMR. Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 57

600

A 550 B ilf. — 500 8°0• ?f ' ~„PQ o - 450 n

400 . `w 350 a r~

ö 300 56° 0' N ö~ - • Diff. Mean of Depth Int. PC [gClm2j - Above 1.5 250 i 1- 1.5 ® 0.5- 1 o- 0.5 200 O -0.5 - 0 ® OVER 35 O -1-.0.5 150 O -1.5- -1 03- 0.4 .[.~ O -2--1.5 02- 03 v~ U -2.5- -2 ▪ 0.1 - 02 100 ilc ° f4 "' ~ h;" t L - --2.5 Q -00 0.1 -011 q W U -4- -3 50 /Iit~N' °•t å- V -' O -e- -4 O -03--02 ~~ , -8- -e O -0.4-03 ~ ~ IIII Below -8 0 -05 --0.4 0 � WIDER -0.5 0 100 200 300 400 (kilometer)

58 0,1-

56.0'N —

5,181

t e F ]] [ }6'~ ]0 E

Fig. 9. Scenario RN: The reduction (scenario RN — reference) of the time mean vertical integral of phytoplankton biomass (gm-2 of carbon; cf. Fig. 5) 2000-07-01...2000-08-31: (a) from NORWECOM (b) from MIKE3 (c) from FIMR. 58 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Solland & Westerlund MERI No. 49, 2003

600 ~~; A 550 B .8° 0' - .%~ o)(.;; 0 . 500 y • 450 ' - i? rF t vf . It ~~; 4 ,~(1 : 4 i_. 400 ,Y_<_P }.. OS j J~ , ~/ s :1 350 ;-L

0.121 615 C

012

002

005

004

0 02

-002

-009

-012

-616

-62

Fig. 10. Scenario RN: The reduction (scenario RN — reference) of the maximum of the vertical integral of phytoplankton biomass (gm-2 of carbon; cf. Fig. 6) for the year 2000: (a) from NORWECOM (b) from MIKE3 (c) from FIMR.

Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 59

600

550

A 500 B 450

400

350 iv A 300 ö 6° / Dill Prim. Prod. [gC/m2y1 250 ~ ri Above 12 8- 12 200 4- 8 0- 4 -4 - 0 150 (♦ OVER 10 -8 - -4 (♦ 2- lu -12- -8 MI 100 -16--12 -2. -20--16 I~ -4- -2 -24--20 ~ -6 ° -° 50 CI .a. -6 I= -28 - -24 O a6- -a -32--28 O 5--10 Below -32 Q .20.-15 0 (♦ UNDER -20 0 100 200 300 400 (kilometer)

Fig. 11. Scenario RN: The reduction (scenario RN — reference) of the total annual depht-integrated primary production (gm-2 of carbon): (a) from NORWECOM (b) from MIKE3.

600 A B 0 Jo°' 8' ; 500 ^ r _ -. - - a ; n ~_r~ _ --I ‘s--- _~ I ~ 400 C , t_ if i

56°0

..-1 ' ~ Diff. IN/IP ratio [molar] O OVER 200 200 ~ +o o Q 50- 200 f Above 2 O 30- 50 Q ~_ ~ s'; Mg 1 - 2 =I 20- 30 0.5 - 1 O 15- 20 ~ ~/~~ • J. ~ 10- 15 60. 0- 0.5 O 5-10 100 1.:°: «.-: In -0.5 - 0 0 0- 5 O -5- 54 tit N ; ,., -1 - -0.5 -2- -1 O 15. 40 ö -4 - -2 ~- O -30--20 %—~~~ Below -4 O -60- -00 0 O -000- -60 1.1 UNDER -200 0 100 200 300 400 (kilometer)

I ~ ~ 100 C 50 64.0°N - ~ 30

62.D°N - ~ 15 , 60.0°N - ,

,- t0 56.0'N

-10

56.0-II — -50

-200 54.0°N -

14°F 18°F 30 E

Fig. 12. Scenario RN: The reduction (scenario RN — reference) of the time mean of surface (0-10 m) inorganic molar N/P ratio (cf. Figure 4) in the reference runs for 2000-07-01...2000-08-31: (a) from NORWECOM (b) from MIKE3 (c) from FIMR.

60 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Soiland & Westerlund MERI No. 49, 2003

4.2 Scenario RP: Reduction of phosphorus from river runoff

As in Section 4.1, the flux of inorganic phosphorus from river runoff was reduced by 30 % for all rivers, and the consequences of this scenario were evaluated by running the models for one year. The results are depicted in Figures 12-15 as a difference to the reference run.

600 - ~ A 550 B 3. o• r' 500 4

450 - • ( H tT '~ 400 - ' • 350 - ~ ;r+"

300 - 56'U• 1 • Off. Mean of Depth Int. PC lgC/m2] - Above 1.5 250 - MI 1 - 1.5 0.5 - 1 f `~ _] 0- 0.5 200 - -0.5- 0 OVER 0.5 i~~ 0 -1--0.5 150 -1.5- -1 - 00-0.4 APO( 0 -2- -1.5 Q 02- 0,0 ra - . -2.5- -2 ~ 0.1 02 100 0 -3--2.5 N , FT -4 - -3 0 54' ST Q -02--04 50 a- O -6- -4 ~ 03-02 ~ IN -B- -8 0 !-`-°`i.. Below -8 1=1 _ UNDER -05 0 100 200 300 400 (kilometer)

11,0

ei 64.0'1 — 0015 C tea ■ 62.0'5 — . . 60.0'5 — . 1.5

58.0'tl — ■

—001 56.0'1 —

54.0'5 — I 14°E 1B`E 22`E 26°E 30 E

Fig. 13. Scenario RP: The reduction (scenario RP — reference) of the time mean vertical integral of phytoplankton biomass (gm-2 of carbon; cf. Fig. 5) 2000-07-01...2000-08-31: (a) from NORWECOM (b) from MIKE3 (c) from FIMR.

Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 61

600 ;~. 550 A !B°0' r) ] y ~. B 500 ..... • ~~~~ ~ _ 450 ~ r ~) r~ .r., '& 400 ',5.(:/'. ö'. 350 ö.o<~ . ,ti >> 7 ö 300 i56° 0l' I . . "J'~`-j• Duff. Max of Depth Int. PC (gC/m2] Above 1.5 `i — 250 4 MI 1. 1.5 ® 0.5- 1 Q ~ •~~ 71 0- 0.5 200 I -0.5- 0 - OVER 05 ••• -1--0.5 150 0 -1.5- -1 - 00- 04 ~ ~ ~'.•j -2--1.5 02- 03 eb•.. , -- - - -2.5 - -2 6.1-02 100 C--,) -3 - -2.5 54 w 0 -4 - -3 p ~~;4~ -02--0.1 50 mi®!-%ff. IJ -6 - -4 ro ZN 0 00--02 .- T-,‘. MI Below -8 -03- 04 0 - UNDER -00 0 100 200 300 400 (kilometer)

0.10

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0.1 C 62.0 0 09

0.02 60.0°N —

50.0.21-

56.0.5 -

54.0°N I I I 14°E 16°E 22°E 26°E 30 E

Fig. 14. Scenario RP: The reduction (scenario RP — reference) of the maximum of the vertical integral of phytoplankton biomass (gm-2 of carbon; cf. Fig. 6) for the year 2000: (a) from NORWECOM (b) from MIKE3 (c) from FIMR.

600 I'► A 550 `-- B ~A/, ~I ~ 500 8`~~ c-- 450 j , 0 „~

400 o- , i " ✓ %~I l l(` `/v r. 350 d d 300 ö 66~'Ö" h • ~ ~ ~r l ~ Ditt Prim. Prod. (gC/m2/y] 250 i Li Above 12 ~ it2I 8 - 12 200 , :?~ ,p 4- 8 . `•~ I 0- 4 ~. I -4- 0 150 1J 1 OVER lo r '~,Y I I -8 - -4 ; a4 ,-,_5`- I—I -12- -8 Q 2- 10 ',, © 0- 2 100 l~l" I—I -16--12 D j . I-1 -20--16 I= -4- -2 54° lil'‘, t w I 1 -24 - -20 d - - -0 50 6 -28- -24 O -0' å O ~0~ I—I O -10- -a Ö ® -32 - -28 0 NI Below -32 0 WIDER -20 0 100 200 300 400 (kilometer)

Fig. 15. Scenario RP: The reduction (scenario RP — reference) of the total annual depth-integrated primary production (gm-2 of carbon): (a) from NORWECOM (b) from MIKE3. 62 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Sr iland & Westerlund MERI No. 49, 2003

A B

Diff. IN/1P ratio [molar] OVER 200 ~- 50 ' 2m II Above 2 I- 30. so IS 1- 2 ~ 20 ' . I= is. 20 0.5 - 1 ~ lo- u I I 0 - 0.5 I= 5- 10 -0.5 - 0 O 0- 5 I 1 -1 --0.5 -2- -1 Q 45. 40 I I Q -20- ls MI -4 - -2 Q -30. .20 Below -4 Q S0. .30 '200' 'SO - mom .200 100 200 300 400 (kilometer)

1000 C 200 50 64.0°1 —

,20 "04 ,is ,10

60.0°1 — ,

58.0`1 — ~ -15 -20 56.0°R — _5O -2a0

•4°E 22°E 26°E 30 E

Fig. 16. Scenario AN: The reduction (scenario AN — reference) of the mean of surface (0-10 m) inorganic molar N/P ratio (cf. Figure 4) for 2000-07-01...2000-08-31: (a) from NORWECOM (b) from MIKE3 (c) from FIMR.

4.3 Scenario AN: Reduction of atmospheric deposition of nitrogen

In the scenario for the atmospheric nitrogen deposition, a uniform reduction of 30 % was applied over all modeled areas. Figures 16-19 depict the difference of this scenario to the reference run, when integrated over the whole year. Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 63

600

A 550 • B

500

450 _' 1 • P 400

350 v v ö 300 Diff. Mean of Depth Int. PC [gC/m2] ! i = Above 1.5 v 250 I 0 1- 1.5 ) 117] 0.5- 1 0- 0.5 200 -0.5 - 0 ,1 ~ I I -1 --0.5 OVER 0.5 ~ - 0.4- 0s 150 ,,,:, 0 -1.5- -1 - 03-0< 0 -2--1.5 - ~ 02- 03 100 ;'a •.. - = -2.5- -2 ~ 0.1- 02 ., = -3--2.5 O oa - 0.1 N = -4- -3 0 -01 -0.0 50 —r „ L O -6- -4 O 02.0.1 - -8- -6 0 03--0.2 O o4--03 0 Below -8 Q -05--0.4 - UNDER -05 0 100 200 300 400 (kilometer)

Fig. 17. Scenario AN: The reduction (scenario AN — reference) of the time mean vertical integral of phytoplankton biomass (gm-2 of carbon; cf. Fig. 5) 2000-07-01...2000-08-31: (a) from NORWECOM (b) from MIKE3 (c) from FIMR.

64 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Solland & Westerlund MERI No. 49, 2003

600

A 550 B ',å~ .8°0' s FA' ish 500 r

450 ;i ~

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0 05 60.0°N —

,-0 Cri

56.0°N ~

56.0°N —

54.0`5

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Fig. 18. Scenario AN: The reduction (scenario AN — reference) of the maximum of the vertical integral of phytoplankton biomass (gm-2 of carbon; cf. Fig. 6) for the year 2000: (a) from NORWECOM (b) from MIKE3 (c) from FIMR.

~ 600

A 550 B ~! ...8° 0'r ---- j 500 ~ t - -y ~ft~ ~ C' - 450 / - 4 LI-,- 400

.. 350 ._-- 115 lia. w ö 300 56' 0' • Diff Prim. Prod. [gC/m2/y] v 250 ' iI. Above 12 k 8- 12 200 ▪ 4- 8 ~.,l, I 0- 4 I I -4- o 150 I -8- -4 [- OVER 10 - I h9 -12- -8 ~ 2- 10 4 O MI 0- 2 100 °ur-., I 1 -16--12 -2- 0 I-1 -20--16 -54i 9,;_t___ 3'. w I -24--20 i=1 -0- A 50 , a_. 1~~, 6 0 -28--24 -e ~ 0 I= -32 - -28 Q -ts --10 0 1'; ME Below -32 = '20"-1s _ utA]ER -20 0 100 200 300 400 (kilometer)

Fig. 19. Scenario AN: The reduction (scenario AN — reference) of the total annual depht-integrated primary production (gm-2 of carbon): (a) from NORWECOM (b) from MIKE3. Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 65

Table 2. Transnational transport across the SKAGEX-H section (Dybern & al. 1994), in kilotons of nitrogen or phosphorus/year, respectively. The full transport for Scenario 0 is given, whereas the transports for the reduction scenarios are deviations from the 0-scenario.

Transport in scenarios NORWECOM variable 0 RN RP AN DIN, ktons N/year 1152 -182 +16 -76

DIP, ktons P/year 102 +0 -3 +1

Table 3. Nutrient flux at section in Kattegat/Skagerrak, 57.3° N (1 January-15 December 2000). Note: Negative values represent fluxes from Skagerrak into Kattegat. The full transport for Scenario 0 is given, whereas the transports for the reduction scenarios are deviations from the 0-scenario.

Transport in scenarios MIKE 3 0 RN RP AN DIN, ktons N/year -62.86 +0.4 +0.36 -1.35

DIP, ktons P/year -9.58 +0.14 +0.2 +0.8

4.4 Cross-boundary transport

The effect of the scenarios on the transnational transport of nutrients has been evaluated for the danish part of SKAGEX-H section (NORWECOM; Table 2) as well as for a section in the Kattegat (MIKE 3; Table 3), describing the Baltic-North Sea exchange.

5. CHANGES IN INDICATORS AS AN EFFECT OF REDUCTION SCENARIOS

The experiments of the effects of nutrient reduction scenarios have given an interesting depiction of what might happen as a result of different political eutrophication abatement strategies. In what follows, we will discuss the effect of abatement strategies on the chosen ecological indicators.

5.1 The inorganic N/P ratio

The inorganic N/P ratio during the production season is sensitive to initial conditions and, depending on the degree to which inorganic nutrients are utilised by primary production, may tend towards very high or very low values, i.e. strong phosphorus or nitrogen limitation. This is seen happening in the NORWECOM model results in Figure 4, and to a weaker extent in the MIKE3 and FINECO-1 results. In the Baltic Sea, the ratio highlights the different ecological provinces (Figure 4(c)). In the North Sea, the N/P ratio in the German Bight/western Denmark coast is strongly affected by the RN scenario in the NORWECOM model results and, to a lesser yet significant extent, the MIKE3 results, which both show a reduction in Elbe outflow area. In NORWECOM results, the reduction effect is seen to propagate along the Danish coast, bringing the N/P ratio closer to the optimum for phytoplankton growth (close to the Redfield ratio). Significant reductions are also seen in the Kattegat area in NORWECOM results, but the effect is relatively small in MIKE3 model. In the Baltic, the most clear effects are seen in the phosphorus-limited Bothnian Bay and the Gulf of Riga. The effects of scenario RP are also seen clearly in the western Denmark area and to some extent, on the Kattegat side in the NORWECOM results. A smaller influence of this scenario is seen in the MIKE3 66 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Solland & Westerlund MERI No. 49, 2003 results, which indicate an increase of the N/P ratio in certain river deltas in Kattegat. In the Baltic, the highest sensitivity of the N/P ratio to phosphorus reduction is found in the Bothnian Bay. In the AN scenario, the N/P ratio is clearly reduced (i.e. the phosphate limitation decreased) in many areas of the NORWECOM results. MIKE3 results show a more horizontally homogeneous reduction in the N/P ratio. The response of FINECO-1 is similar to the RN scenario, i.e. the Bothnian Bay and Gulf of Riga show the most clear reductions in the N/P ratio.

5.2 Summer biomass

The mean summer biomass, as displayed in Figure 5, has its maximum in the central Skagerrak, Elbe delta and south-western Norwegian coast in the North Sea models. In the Baltic Sea, a seemingly robust maximum in the summer biomass appears in the entrance to the Gulf of Finland (FINECO-1) and in the Belt Sea (MIKE3). The RN scenario mostly affects the biomasses very close to river entrances in both NORWECOM and MIKE3 (Figure 9). In MIKE3, the effect is seen in the Elbe river plume, whereas in NORWECOM, a similar effect is seen in the transition area between the Baltic and the North Sea (east coast of Denmark). In the Baltic, FINECO-1 results indicate only minor changes in general, with most significantly a biomass reduction in the Swedish east coast and a biomass increase most notably in the Gulf of Riga. An analysis of the different plankton groups (not shown) traced the small biomass increase back to a coincinding overall increase in the abundancy of cyanobacteria and diatoms, and a reduction in the amount of flagellates, albeit with local modifications depending on the smaller-scale nutritional and hydrodynamic conditions. The RP scenario leaves a long fingerprint in the mean summer biomass, extending from the Elbe plume to the southern Norwegian coast in the NORWECOM model (Figure 13), whereas there is only a minor effect in the Elbe plume in MIKE3. In the plumes of smaller rivers, hardly any effect is visible, apparently since most of them are phosphorus-limited to begin with. In the Baltic, FINECO-1 results indicate only minor changes at the entrance of river plumes in the southern Baltic. The reduction in the atmospheric nitrogen deposition (scenario AN) has the largest effects on the summer biomass in the middle of Skagerrak, in both MIKE3 and NORWECOM (cf. Figure 17). An apparent explanation is the maximum of the summer biomass, which is found in this area in both models and is reduced by the reduction in the atmospheric deposition of nitrogen. The FINECO-1 response reflects the large-scale pattern of atmospheric nitrogen deposition, although with only minor relative changes and modifications that depend on the local nutrient conditions in each deposition area: the biomass in the south-eastern Swedish coast is reduced, whereas the eastern Baltic Proper, Gulf of Riga and south-eastern Gulf of Bothnia show an increase in biomass, again to be traced back to an increase in model cyanobacteria.

5.3 Annual maximum of biomass The biological "hot spots" of the NMR area are indicated by the annual maximum of the vertically integrated biomass as depicted in Figure 6. In the Kattegat/Skagerrak area, the maxima appear along the Danish west coast, the Norwegian south-western coast, in the middle of the Skagerrak trench as well as in most of the central Kattegat, as most clearly seen in NORWECOM results. In the Baltic, the most distinct maximum appears in the Gulf of Finland and the minimum in the Bothian Bay. In the RN scenario (Fig. 10), the maximum biomass is clearly reduced in the Elbe plume (MIKE3) as well as along the Norwegian coast (NORWECOM). In FINECO-1 results, minor increases in the north- eastern Baltic Proper and eastern Gulf of Finland as well as decreases in the south-western Baltic Proper and Gulf of Bothnia of the annual maximum are observed. In the phosphorus reduction scenario, on the other hand, the biomass maximum is strongly reduced along the whole trajectory from the German Bight to the Bergen area in the NORWECOM results (Fig. Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 67

14). The MIKE3 results, on the other hand, show little overall change outside the river plume areas, with almost negligible changes observed in the FINECO-1 results. For the reduction in atmospheric deposition, there is a significant decrease of the maximum biomass in both NORWECOM and MIKE3 (Fig. 18) in the Norwegian trench area. NORWECOM shows a pattern similar to the summertime mean biomass reduction, but on a wider area. MIKE3 has a somewhat stronger response to the AN scenario in the maximum biomass than in the mean biomass, in particular in the northern Skagerrak. In FINECO-1 results, there are only minor changes that follow the east-west pattern seen in the summer biomass results. The actual value of the annual maximum of biomass seems to be sensitive to the ecosystem model formulation as well as northern boundary in the North Sea models. There is, however, clear systematics in the spatial distribution of the annual maximum, which may carry over to observable phenomena.

5.4 Annual primary production

The maxima of annual primary production are mostly found along the coastlines in the models (cf. Fig. 7). NORWECOM has a strong maximum along the Norwegian coast and minor maxima in the central Kattegat area and the Skagerrak trough, whereas MIKE3 shows additionally the production maximum associated with the German Bight riverine waters. The Skagerrak maximum is a bit broader in MIKE3 than in NORWECOM. The strongest effects of the river nitrogen flux reduction scenario (RN; Fig. 11) is the increase of production in the Baltic boundary and the upper Norwegian coast in NORWECOM results, and the reduction in the German Bight in MIKE3 results. MIKE3 results show an overall reduction in the primary production, whereas there is a small increase in NOREWECOM results in the western Denmark coast. The RP scenario causes a significant reduction of production in the western Denmark coast in NORWECOM results (Fig. 15), whereas in MIKE3 results, the effect is mostly limited to the Elbe outflow area and other river plumes, with seemingly little systematic effect in other areas. The reduction in the atmospheric nitrogen deposition is seen to lead to mostly a reduction in the annual primary production in the MIKE3 results and in the central North Sea in the NORWECOM results (Fig. 19). However, the NORWECOM results also show an increase in production in the Kattegat/Skagerrak transition zone, coastal zone of Denmark as well as southern Norway as a result of a decreased atmospheric deposition.

6. CONCLUSIONS

Almost five years ago, Aksnes & Selvik (1998) noted the potential for taking numerical models into environmental management in the NMR area. Since then, there has been considerable progress in this direction. The NO COMMENTS project has highlighted the early potential of the use of operational 3D physical- chemical-biological ocean models in support of environmental decision making, in particular as concerns the ecosystem effects of reductions in land-borne nutrient forcing. Although this is a field of science that is still in a somewhat exploratory state compared to e.g. the atmospheric pollution transport research, it has been shown here that the approach is technically feasible and merits a further development of ecological models as well as the derived information products. During the project, it was possible to cover most of the Nordic coasts through the inclusion of several different models, which had a special focus on the specific ecosystem characteristic of their target areas. At the present state, one model alone would not have been sufficient to cover the differing ecosystems in the whole area of interest. Because of the relative simplicity of the NO COMMENTS ecosystem models (1-3 functional phytoplankton groups) compared to the complexity of the marine ecosystem (thousands of phytoplankton species as well as the whole food web spanning at least seven orders of magnitude in 68 Stipa, Skogen, Hansen, Eriksen, Hense, Kiiltomäki, Soiland & Westerlund MERI No. 49, 2003 physical size), it must be obvious that the present model results are a coarse representation of the marine ecosystems. Indeed, the models display a certain inter-model variability in their responses, which can to some extent be traced back to differences in the ecosystem model buildup and the setup of initial and boundary conditions. For example, there is at least in the Baltic Sea a large pool of dissolved organic nitrogen (DON), whose role in eutrophication has been somewhat debated (Eilola & Stigebrandt 1999, Elmgren & Larsson 2001). While NORWECOM and FINECO-1 only consider the forms of dissolved inorganic and particulate nitrogen, MIKE3 divides nitrogen into pools of inorganic, phytoplankton, detritus (bioactive DON and real detritus) as well as non-bioactive, remaining DON, which is not included in simulations. Also the specification of boundary conditions between the North Atlantic and the North Sea, as well as between the Baltic Sea and the North Sea is a difficult issue in the models and is known to give rise to uncertainties in model results at least near the boundaries of the model domain. Such differences in model formulation provide at least a partial explanation for difference seen in the model results. A thorough evaluation of the reasons behind the differences between the models would require a more in-depth approach than was possible during this reduction experiment. However, the internal consistency of each model result has been validated by all partners during the reduction experiment process. It seems that the remaining differences seen in the present results can be narrowed down in the near future. However, there is good reason to believe that the changes seen in the present results are indicative of the true response of the ecosystem, and therefore are already useful for enviromental decision making. For example, the direction of the reduction-induced changes is consistent with simple inferences that can be made from the initial state of the sea area and the driving effect — e.g., the reduction in phosphate input to a phosphate-limited area such as the Bothnian Bay will not cause a large change in the N/P ratio, whereas a reduction of nitrogen input will. In general, the model results showed relatively small short-term sensitivity to reductions in the land- and airborne fluxes of nutrients. The limited spatial extent of the short-term reduction effects is consistent with the scaling arguments of e.g. Stipa (2002b). However, the whole spatial pattern of the area influenced by nutrient reductions in the North Sea, for example, would be difficult to infer without the models here applied. The western coast of Denmark (and the German Bight in general), Kattegat area as well as Gulfs of Riga and Bothnia appeared as areas of highest short-term sensitivity to nutrient runoff reductions. The German Bight receives the highest nutrient input of the modeled area, which provides an explanation for its sensitivity to reductions. The Gulf of Riga, on the other hand, is easily flipped between nutrient reduction patterns, as seen in the results of the NMR-funded Gulf of Riga project (cf. Tamminen & Seppälä (1999)). The computational constraints on the spatial resolution in the Gulf of Finland may mask some of the sensitivity there. One of the interesting observations in the scenario results was the projected increase in Baltic summer biomass as a result of reductions in nitrogen input. Such a causal relation has been put forward several times during many decades of the environmental debate in the Baltic Sea area. In anticipation of forthcoming rapid improvements in the model description of the ecology of nitrogen-fixing cyanobacteria, we state that the present result is consistent with the present level of formal understanding of nitrogen fixation, which was used to formulate the model, but the modeled response may for the time being differ at least in magnitude from the actual response found in Nature. In summary, the reduction experiments in this report have shown that three-dimensional coupled physical-biological models can be used to look for answers to issues in environmental management. Such models offer a consistent discretisation of both physics and biology that, when applied to community models and appropriately documented for the scientific and management community, can be made fully transparent. A large investment has indeed been made during the NO COMMENTS project towards the production of management-relavant results. There is still work to be done on the improvement and continuous validation of in particular the ecological models, but a complete Short-term effects of nutrient reductions in the North Sea and the Baltic Sea as seen by an ensemble ... 69 convergence of all models is hardly desirable and has not happened in e.g. the climate modelling community. More work is also needed in the derivation of new environmental indicators and in a tighter coupling to decision making models and processes through e.g. common workshops with decision-makers.

Acknowledgements

We are grateful to Eleonor Marmefelt (SMHI) for providing the Baltic river inflow data for the year 2000, and the MARE program for the work they have done in collecting the Baltic nutrient load information.

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