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Importance of ocean circulation in ecological modeling: An example from the North Sea
Journal of Marine Systems 57 (2005) 289 – 300 www.elsevier.com/locate/jmarsys
Importance of ocean circulation in ecological modeling: An example from the North Sea Morten D. Skogen a,*, Andreas Moll b b
a Institute of Marine Research, Pb. 1870, N-5817 Bergen, Norway Institut fu¨r Meereskunde, Universita¨t Hamburg, Bundesstr. 53, D-20146 Hamburg, Germany
Received 11 April 2005; received in revised form 16 June 2005; accepted 16 June 2005 Available online 24 August 2005
Abstract There is an increasing number of ecological models for the North Sea around. Skogen and Moll (2000) [Skogen, M.D., Moll, A. 2000. Interannual variability of the North Sea primary production: comparison from two model studies. Continental Shelf Research 20 (2), 129–151] compared the interannual variability of the North Sea primary production using two state-ofthe-art ecological models, NORWECOM and ECOHAM1. Their conclusion was that the two models agreed on an annual mean primary production, its variability and the timing and size of the peak production. On the other hand, there was a low (even negative dependent of area) correlation in the production in different years between the two models. In the present work, these conclusions are brought further. To try to better understand the observed differences between the two models, the two ecological models are run in an identical physical setting. With such a set-up also the interannual variability between the two models is in agreement, and it is concluded that the single most important factor for a reliable modeling of phytoplankton and nutrient distributions and transports within the North Sea is a proper physical model. D 2005 Elsevier B.V. All rights reserved. Keywords: Primary production; North Sea; Ecological model
1. Introduction There is an increasing number of ecological models for the North Sea. Moll and Radach (2003) provided an overview especially about three-dimensional models that describe and predict how the marine ecosystem of the greater North Sea area functions and how concentrations and fluxes of biologically * Corresponding author. E-mail address: [email protected] (M.D. Skogen). 0924-7963/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2005.06.002
important elements vary in space and time, throughout the shelf and over years, in response to physical forcing. The models differ both in the complexity of the biochemical cycles included, and how the ocean circulation to force the biology is modeled. They are all validated (OSPAR, 1998; Radach and Moll, 2005), believed to different levels of confidence to reproduce the North Sea conditions (OSPAR, 1998; Moll and Radach, 2001), and are also used for management purposes, e.g. to investigate the effects of changes in river nutrient loads (Pa¨tsch and Radach, 1997; Len-
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of the North Sea has a long history, and a lot of data are available, it has proved difficult (Delhez et al., 2004) to conclude one model better than another in general. All models seem to have both weaknesses and strengths. For ecological models, the situations are even more difficult. There have been a few attempts to compare such models (de Vries, 1992; OSPAR, 1998). All models meet general assumptions on, e.g. the mean North Sea primary production, but the differences in both
hart, 2001; Skogen et al., 2004). From the comparison between the models of the greater North Sea and observations, it has become clear that ecosystem models should be three-dimensional and should be coupled with or forced by state-of-the-art circulation models (Moll and Radach, 2003). There have been several big projects to compare and validate different North Sea circulation models (e.g. Proctor et al., 1997, 2002). Even if physical modeling
external forcing
Northern hemispheric weather system
Atlantic circulation system
Baltic Sea circulation system
Regional weather processes
Exchange of matter with Atlantic
Exchange of matter with Baltic Sea
river inputs
Exchange of matter with atmosphere
underwaterlight
inorganic suspended matter
shelf sea circulation
Solar radiation
internal dynamics
biogeochemical/ ecological processes ECOHAM1
NORWECOM Z=0 (surface)
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Atmospheric input
Atmospheric input Diatoms
Flagellates
mortality
biogenic silicate
mortality
uptake/ production
respiration
uptake/ production
silicate
DIN
phosphate
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pelagic detritus
uptake
pelagic regeneration
sinking
z=H (bottom)
copepod grazing
nutrient exudation
phosphate River Loads
sinking
Phytoplankton
DIN
copepods
soluble excretion
fecal pellets egestion
mortality, predation
dissolved organic phosphorus
mortality, other grazing
pelagic detritus
pelagic regeneration
sinking
benthic regeneration
benthic detritus
z=H (bottom) biogenic silicate
benthic regeneration
benthic detritus
Benthic regeneration
denitrification
burial
Fig. 1. Conceptual model for the North Sea system with the two different biological modules.
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their state variables and area covered have made a proper comparison an almost impossible task. Skogen and Moll (2000) compared the interannual variability of the North Sea primary production using two state-of-the-art ecological models, NORWECOM (Skogen et al., 1995) and ECOHAM1 (Moll, 1995, 1998; Wei et al., 2004). The conclusion in that paper was that the two models agreed on an annual mean primary production, its variability and the timing and size of the peak production. Also the integrated influence of the river inputs was equal, even if some spatial differences were apparent. The interannual variability could in both models to a large extent be explained from differences in the physical conditions between the years. On the other hand, the physical process that triggered the differences in the primary production between the years was not the same, and there was a low (even negative dependent of area) correlation in the production in different years between the two models. From these results, it was stated that changes in the physical conditions and forcing resulted in a large variability in the primary production in the North Sea, and that a proper circulation model, including both a realistic horizontal advection, exchange with the Atlantic, and a proper simulation of the vertical density structures was essential for primary production studies in that area. In the present work, these thoughts are brought further. To try to better understand the observed differences between the two models, the two ecological models are run in an identical physical setting. This is done by including the biochemical part of ECOHAM1 in the physical setting of NORWECOM, such that the resulting model have two ecological options with the same underlying physical model (see Fig. 1). With such a setting, it should be possible to identify to which order the observed differences in Skogen and Moll (2000) was due to the different physical models, or the differences in the biochemical cycle. Also, such an exercise can show the importance of a proper physical model to force ecological models. This is of special importance when used for management purposes. In the present study, both models have been run for 10 different years (1985–1994), with identical ocean physics, river inputs and light. To isolate the direct effects of interannual variability in the ocean physics to the ecological models, both river inputs and light are fixed to 1990 values in all simulations, whereas
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only surface pressure and wind forcing remain variable. It should be noted that in all development and tuning of ecological models, there is a dependency between the physical model and the biological parameter settings. When using ECOHAM1 in this new completely different physical setting (it is normally forced with daily mean currents, but in this work it is directly coupled to a tidal resolving physical model), too low primary production results appeared. One parameter, phytoplankton mortality rate, was tuned to get proper mean annual North Sea primary production compared to NORWECOM. There are still some difficulties due to low phytoplankton values in the English Channel in the ECOHAM1 results which we ignored in the present work, but no further parameter tuning have appeared since we treat this study as a comparison, not a validation study.
2. The models The study has focused on the years 1985–1994. The first simulation started on December 15, 1984, and the models have been run one by one year with re-initialization every December 15. The models have been run on the same grid with identical forcing, initial fields and open boundaries. To ease the analysis, the monthly mean river loads, and the annual light cycles were identical for each year. One can argue that a re-initialization every December 15 means that the models are not in proper dynamic balance, and that the models might give very different scenario effects in cases where multiyear scenario runs are needed due to the timescale of the changes in the model domain (e.g. to include effects from the sediment pools). Both models can be used for multi-year simulations, but to be consistent with the set-up in Skogen and Moll (2000), the reinitializations are kept. In addition, the main focus in the present study is on interannual variability and effects from changed circulation. Like the use of identical annual cycles of light and river loads, the re-initialization highlights this effect. 2.1. The physical model The two physical models used in Skogen and Moll (2000) has been compared in a long-term modeling
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The model is one out of a set of four marine biogeochemistry and ecosystem community models hosted at the Max-Planck-Institut fu¨r Meteorologie in Hamburg to provide central support for the German and European climate research community and can be downloaded (http://www.mad.zmaw.de/). The aim was to quantify the annual primary production under actual circulation and solar radiation forcing (Moll, 1998) and to describe concentrations and fluxes of biologically important elements in space and time including a full validation (Moll, 2000). Based on ECOHAM1 (Moll, 1998), dissolved inorganic nitrogen (DIN) as a new state variable and the biogeochemical processes concerning the nitrogen cycle were added (Wei et al., 2004). The new ECOHAM1 including DIN was than applied to the Bohai Sea with very little changes in the model parameters. A schematic diagram of the state variables and processes is shown in Fig. 1. Three partial differential equations describe the spatio-temporal evolution in the pelagic realm for phosphate, DIN and phytoplankton. Phytoplankton is represented by one state variable and the model formulations are based on phosphorus and nitrogen to limit the phytoplankton production. Grazing of phytoplankton by zooplankton is treated dynamically due to a formulation according to Michaelis–Menten including a threshold value below phytoplankton grazing ceases. The model is
study of variability, circulation and transports in the North Sea (Smith et al., 1996). This study concluded that the models showed similar patterns of variability in volume transports, that the mean values compared well in the southern North Sea, but that the NORWECOM physics had somewhat higher transports in the northern North Sea. For the present study, it was chosen to use the physical module from the NORWECOM model to run both models using the same physics. That physical model is based on the threedimensional, primitive equation, time-dependent, wind- and density-driven Princeton Ocean Model (POM). The model is fully described in Blumberg and Mellor (1987). In the present study, the model is used with a horizontal resolution of 20 20 km2 on an extended North Sea (see Fig. 2). In the vertical, 12 bottom following sigma layers are used. 2.2. The ECOHAM1 model The ECOlogical North Sea Model, HAMburg, Version 1 (ECOHAM1) is a three-dimensional model system that study nutrient and phytoplankton dynamics. It is the robust German ecosystem model of the North Sea that has been developed at the Institute fu¨r Meereskunde, Hamburg, first, for the simple phosphorus cycle (Moll, 1997), and later extended for an additional simple nitrogen cycle by Wei et al. (2004).
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Fig. 2. Model bathymetry, North Sea 20 20 km2.
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conceptualized for a shelf sea including the shallow sea characteristic for the replenishment of the water column with nutrients from the bottom. Therefore, two ordinary differential equations describe the benthic detritus in terms of phosphorus and nitrogen pools. Underwater light is calculated by a diagnostic ordinary differential equation that includes shelf shading due to phytoplankton.
data from DNMI’s operational wave model, WINCH (SWAMP-Group, 1985; Reistad et al., 1988), are used. Parameterization of the biochemical processes is taken from literature based on experiments in laboratories and mesocosms, or deduced from field measurements (Aksnes et al., 1995; Pohlmann and Puls, 1994; Mayer, 1995; Gehlen et al., 1995; Lohse et al., 1995, 1996).
2.3. The NORWECOM model
2.4. Model set-up, forcing and initialization
The NORWegian ECOlogical Model system (NORWECOM) is a coupled physical, chemical, biological model system (Aksnes et al., 1995; Skogen et al., 1995; Skogen and Søiland, 1998) applied to study primary production, nutrient budgets and dispersion of particles such as fish larvae and pollution. The model has been validated by comparison with field data in the North Sea/Skagerrak in, e.g. Svendsen et al. (1996), Skogen et al. (1997, 2004) and Søiland and Skogen (2000). 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), phosphorous (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) and oxygen (see Fig. 1). The processes included are primary production, respiration, algae death, remineralization of inorganic nutrients from dead organic matter, self shading, turbidity, sedimentation, resuspension, sedimental burial and denitrification. Phytoplankton mortality is given as a constant fraction, and is assumed to account also for zooplankton grazing which in this context is included as a forcing function. Particulate matter has a sinking speed relative to the water and may 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. Initial and forcing data on suspended particulate matter, are taken from Pohlmann and Puls (1994). Remineralization takes place both in the water column and in the sediments. The bottom stress is due to both currents (including tides) and surface waves. To calculate the wave component of the bottom stress,
The forcing variables for the physical model are 6hourly hindcast atmospheric pressure fields provided by the Norwegian Meteorological Institute (DNMI) (Eide et al., 1985; Reistad and Iden, 1998), 6-hourly wind stress (translated from the pressure fields by assuming neutral air–sea stability), four tidal constituents and freshwater runoff. In the lack of proper data on the surface heat fluxes, a Qrelaxation towards climatologyQ method is used (Cox and Bryan, 1984). During calm wind conditions, the surface temperature field will adjust to the climatological values after about 10 days (Oey and Chen, 1992). The net evaporation precipitation flux is set to zero. Initial values for velocities, water elevation, temperature and salinity are taken from monthly climatologies (Martinsen et al., 1992). Interpolation between monthly fields are also used at all open boundaries, except at the inflow from the Baltic where the volume fluxes have been calculated (Stigebrandt, 1980) from the modeled water elevation in Kattegat and the climatological monthly mean freshwater runoff to the Baltic. To absorb inconsistencies between the forced boundary conditions and the model results, a 7-gridcell QFlow Relaxation SchemeQ (FRS) zone (Martinsen and Engedahl, 1987) is used around the open boundaries. The incident irradiation is modeled using a formulation based on Skartveit and Olseth (1986, 1987). Data for global daily radiation from 1990 is taken from a station at Taastrup (Denmark) (Anon., 1991) and used for all years. Nutrients (inorganic nitrogen, phosphorus and silicate) are added to the system from the rivers and from the atmosphere (only inorganic nitrogen). River data (freshwater and nutrient loads) from 1990 are used for all years. These data originates from Rijkswaterstaat (Belgium and the Netherlands), Arbeitsgemeinschaft fu¨r die Reinhaltung der Elbe and
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¨ kologie (GerNiedersa¨chsisches Landesamt fu¨r O many), National Environmental Research Institute (Denmark), The Swedish Meteorological and Hydrological Institute and Swedish University of Agriculture (Sweden) and the Norwegian Water Resources and Energy Directorate and the Norwegian State Pollution Control Authority (Norway). In the lack of data from British rivers, climatological data were used (Balin˜o, 1993). In addition, some extra freshwater is added along the Norwegian and Swedish coast to fulfill requirements to estimated total freshwater runoff from these coastlines (Egenberg, 1993). The initial nutrient fields are derived and extrapolated/interpolated (Ottersen, 1991) from data (obtained from ICES) together with some small initial amounts of algae (NORWECOM 0.10 mg N m 3 for diatoms and flagellates, ECOHAM1 0.20 mg N m 3 for phytoplankton). Nutrient data (monthly means) measured in the Baltic (ICES) are used for the water flowing into Kattegat.
3. Results The horizontal distribution of the integrated annual primary production have been calculated, and in Fig. 3 the 10 years annual mean production for both models are given. In addition, the mean production in each ERSEM box (see Fig. 4) and the integrated
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mean North Sea (boxes 1–10) primary production have been found for each year. All numbers are given in Table 1 (NORWECOM) and in Table 2 (ECOHAM1). Also the standard deviation, referring to the spatial variability of the mean of each box, is given. The last column gives the span: (prodmax prodmin) / prodmean 100% for each box. This number is used as a measure of the interannual variability of the production. When discussing the results we recall the model names for the biochemical setup of state variables as described in Fig. 1.
60
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Fig. 4. The different ERSEM boxes (1–10).
80
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Fig. 3. Mean annual production (gC/m2/year) from the NORWECOM model (left) and the ECOHAM1 model (right).
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Table 1 Production statistics for ERSEM boxes with the NORWECOM model Box/year
85
86
87
88
89
90
91
92
93
94
Mean
S.D.
Span
1 2 3 4 5 6 7 8 9 10 Mean
113 105 113 84 106 157 136 171 224 144 122
122 108 136 94 124 138 140 169 203 154 129
101 89 142 70 116 132 136 176 208 160 120
116 117 121 83 118 181 165 179 170 135 128
114 98 133 69 102 144 157 191 193 119 120
98 101 128 85 105 158 145 177 184 129 121
101 85 128 63 116 136 146 180 210 129 116
109 101 118 70 105 137 132 179 224 144 118
98 94 139 66 140 127 149 185 194 137 122
99 91 122 65 117 129 137 173 189 131 114
107 99 128 75 115 144 144 178 200 138 121
22 20 22 8 25 45 36 43 39 39 45
22 32 23 41 33 38 23 12 27 30 12
Annual production (gC/m2 /year), mean production, standard deviation and the span ((max min) / mean 100%). All numbers (except the span) are point-wise means covering all grid points within a box. The standard deviation is referencing to the spatial variation of the mean production within a box, and the span to the interannual variability of the annual means of a box.
The results using the NORWECOM model varies from 63 gC/m2/year to 224 gC/m2 /year (box averages from Table 1) within the North Sea. The highest numbers are found close to the main rivers along the continental coast in the southern North Sea. In the central North Sea, the production is generally much lower, while the production is increasing northward due to the inflow of nutrient-rich Atlantic water. Separating the production between diatoms and flagellates, there is a north–south gradient in the flagellate production, while there is a more uniform diatom distribution. The highest relative interannual variability in the production is found in box 4 (Central North Sea) with a span of 41%, but there are several boxes with a span of more than 30%. Except for the minimum found in box 8 (Belgian/Dutch coast) (12%), all boxes are reporting on a span of at least 20%. The reason for the low span in box 8 is due to the strong influence of rivers, and that these inputs are fixed in all runs. On the other hand, the same box (8) has a large number for the standard deviation (43 gC/m2/year), since the influence of the river nutrients is highly variable within the box. The smallest standard deviation is found in the Central North Sea (box 4). Focusing on the mean North Sea production the variability is much lower. The span is only 12%, equal that found in box 8. This indicates that low production in one area, is compensated with higher production elsewhere, and that it is difficult to distinguish between years with high and low productivity in the
North Sea only due to changes in the wind-driven circulation. An example of such a compensation of production is between the central North Sea boxes (4 and 5) in 1993. This year box 4 has a very low production, while its neighboring box (5) has its overall maximum. This can probably be explained from a small shift in the modeled circulation. Investigating the seasonal patterns, they are similar for all years with a maximum production in May of around 33 gC/m2/month as a mean for the whole North Sea. This is the same production rate as reported on by Weichart (1980) for the FLEX study in the northern North Sea in the spring of 1976. Between the end of April and the beginning of June, the production was about 30 gC/m2. Comparing the different months between the years, there are only small differences (10–15%). The exception for this is June 1986 and August 1988 when the production is about 30% higher than the mean June and August production, respectively. This increased production is due to a very high inflow of nutrient-rich Atlantic water as nutrients are depleted after the late spring bloom. For the ECOHAM1 model, primary production varies from 86 gC/m2/year to 243 gC/m2/year within the North Sea (box averages from Table 2). The highest numbers are found in the German Bight (box 9), while the lowest production is in the Central North Sea (box 4). The highest interannual variability in the production is found along the Belgian and Dutch coast (box 8) with a span of 50%.
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Table 2 Production statistics for ERSEM boxes with the ECOHAM1 model Box
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89
90
91
92
93
94
Mean
S.D.
Span
1 2 3 4 5 6 7 8 9 10 Mean
105 119 135 95 123 128 89 104 221 183 120
111 115 160 113 124 108 86 77 216 179 123
88 101 157 85 124 119 87 102 243 187 117
118 131 142 103 116 147 121 113 163 167 125
106 110 144 90 119 117 102 131 207 159 120
92 113 147 112 125 141 111 131 190 161 126
94 105 142 92 127 126 109 126 215 158 120
98 113 152 87 125 108 86 113 221 187 119
101 111 155 80 151 113 97 99 206 164 120
105 114 132 90 139 120 85 88 221 171 118
102 113 147 95 127 123 97 108 210 172 121
9 18 25 12 26 44 38 58 57 53 43
29 27 19 35 28 32 37 50 38 17 7
Annual production (gC/m2 /year), mean production, standard deviation and the span ((max min) / mean 100%). All numbers (except the span) are point-wise means covering all grid points within a box. The standard deviation is referencing to the spatial variation of the mean production in a box, and the span to the interannual variability of the annual means of a box.
Outputs from the two models have many similarities with low production in the central and northern North Sea, with increasing production towards the coasts. The mean annual North Sea production is 121 gC/m2/year with both models. Except for the Belgian and Dutch coast (box 8), the interannual variability using ECOHAM1 are in the same range as for NORWECOM. Also for the spatial difference within the boxes (as given by the standard deviation), all numbers from ECOHAM1 are comparable to those from NORWECOM except for somewhat higher values in the coastal boxes (8, 9 and 10). Also the correlation in production between different years for the two models are generally high (see Table 3). In five of the boxes (2, 4, 6, 7 and 10) r N 0.80, and only in one box (8) it is below 0.70. However, this box is exceptional as will be discussed below. Taking the mean over the whole North Sea has the lowest correlation (r = 0.63), but when removing the box 8 from the calculation the correlation is r = 0.81. Apparently, there is a shift in the timing of the peak (monthly) primary production with ECOHAM1 compared to NORWECOM. In ECOHAM1, the maximum production is found in June, except for 1988
when the peak was in May. However, focusing on daily production rates, the peak is almost identical in both models, with a maximum around June 1. The monthly shift is therefore due to differences in the shape of the curve of daily production. Except for the monthly production peak shift in 1988, there are less interannual differences between the months using ECOHAM1, but also with ECOHAM1 June 1986 and August 1988 has the highest monthly production rates. The largest differences between the models are seen in the English Channel and along the Belgian and Dutch coast (box 8). In these areas, ECOHAM1 have very low production, despite the high loads of river nutrients. The reason for this has been examined, and can be explained from the differences in maximum production rate between the two models. In ECOHAM1, this rate is 1.5 (day 1), while in NORWECOM it is temperature dependent with a value of 2.4 at 168. The lower value gives a downstream shift in the production. By using ECOHAM1 in its normal physical setting, these low productions are not seen, and by increasing the maximum production value to that used in NORWECOM in the present setting, a similar pattern to that for NORWECOM results. How-
Table 3 Correlation in production between different years for ECOHAM1 and NORWECOM Box
1
2
3
4
5
6
7
8
9
10
Mean
0.73
0.88
0.71
0.86
0.70
0.83
0.84
0.64
0.75
0.85
0.63
130 NORWECOM ECOHAM1
128 9
126
annual mean production
200
ECOHAM1
10
3
150 5
6
2 1 100
4
8
124 122 120 118 116
7 114 112
50 50
100
150 NORWECOM
200
250
110 1984
1985
1986
1987
1988
1989 1990 Year
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1992
1993
1994
1995
Fig. 5. Comparison of mean annual production (gC/m2/year) for the NORWECOM versus ECOHAM1 model (left) in the space domain using the regional annual production for the defined 10 different boxes of Fig. 4 including the standard deviation of the 10 years, and (right) in time domain presenting the 10 years time series of the mean North Sea production.
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Table 4 Correlation in production between different years for ECOHAM1 with NP and P limitations Box
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3
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5
6
7
8
9
10
Mean
0.99
0.99
0.98
0.99
0.91
0.99
0.96
1.00
1.00
0.90
0.97
ever, as this is not a validation study but a model sensitivity study, no further tuning of parameters have been done. In general, the production using NORWECOM is higher in the southern North Sea and along the British coast (boxes 6, 7 and 8) than by using ECOHAM1 (Fig. 5, left panel), and there is a better agreement between the two models in the first half of the period (1985–1989) than for the second part (Fig. 5, right panel).
4. Discussion One of the main applications of ecological ocean models are to be used for management purposes, such as scenario simulations, e.g. examining the effect of changes in anthropogenic forcing like river nutrient inputs. Also, the effect of a possible climate change to the marine environment is of vital interest, and will to a large extent depend on reliable models and their predictions. However, before a model can be used for such studies it must be properly validated and able to reproduce today’s conditions. An important aspect of this is the interannual variability. Even if two models agree on the mean conditions and patterns, they are likely to give quite different answers to management efforts and the effect of climate change if the interannual signals due to changes in the atmospheric forcing is not consistent. Even if the two models (NORWECOM and ECOHAM1) to a large extent agreed in a previous study (Skogen and Moll, 2000), their disagreement in the interannual variability will introduce large uncertainties when either of them is used for predictions of the effect of changed forcing. It is therefore essential to understand their different behavior. The present study
shows that to first order the differences in ocean circulation (that forces the biological models) can explain the observed disagreement in interannual variability, and that the different biological formulations are of less importance when focusing on the changes in primary production. This conclusion can be further emphasized with two additional experiments. In the first one, a simplification of the NORWECOM model is done, such that it is run with only one phytoplankton (flagellates) and two nutrients (inorganic nitrogen and phosphorous), and in the second one the ECOHAM1 model is run with phosphorous limitation only. In the ECOHAM1 results, there is an increase in the mean North Sea production of 16% when only Plimitation is performed. The increase is largest in the northern North Sea, telling that N-limitation is more likely to occur in these areas, while there are no change in the German Bight (box 9). However, despite the changed level of production, the interannual variability is almost undisturbed with the two different versions of ECOHAM1. This is shown in Table 4, where most correlations are found to be very close to 1.0. There is a decrease in the total production between the two versions of the NORWECOM model of more than 15%. The decrease is highest in the northern North Sea, where the diatom production in the full model was highest. However, when comparing the flagellate production only, there is an increase of about 40%. This increase has an almost uniform distribution. Nevertheless, despite the changed level of production, the interannual variability is almost undisturbed with the two versions of the NORWECOM model (see Table 5) like it was with the ECOHAM1 model. From all these simulations, we conclude that the single most important factor for a reliable modeling of
Table 5 Correlation in production between different years for NORWECOM with one and two phytoplankton state variables Box
1
2
3
4
5
6
7
8
9
10
Mean
0.89
0.95
0.83
0.96
0.86
0.99
0.95
0.96
0.97
0.98
0.97
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phytoplankton and nutrient distributions and transports within the North Sea is a proper physical model. Before a further investigation into biological process formulations and parameter settings, one has to ensure that the model is driven by a high quality and thoroughly validated physical model, including proper atmospheric forcing. Only with the physics in place, it becomes interesting to examine the responses of changes in forcing. There is no guarantee that two models that give similar results on average will react in a consistent way. Skogen and Moll (2000) showed this, and without saying anything about the correctness of any of the models in that study, the present study have proved that the physical circulation field is the main driving mechanism for such models. Acknowledgment This work was in part supported by the Norwegian Research Council within the project MONCOZE (contract number 143559/431) for M.D. Skogen, and BMBF project GLOBEC Germany (BMBF 03F0320E) for A. Moll. References Aksnes, D.L., Ulvestad, K.B., Balin˜o, B., Berntsen, J., Egge, J., Svendsen, E., 1995. Ecological modelling in coastal waters: towards predictive physical–chemical–biological simulation models. Ophelia 41, 5 – 36. Anon, 1991. Solar radiation and radiation balance data, 1990. The World Radiation Data Centre, St. Petersburg, Russia. Balin˜o, B.M. 1993. Nutrient inputs to the North Sea, Skagerrak and Kattegat. River concentrations and loads from 1980 to 1990. Tech. rept. HOV-senteret, Høyteknologisenteret, Bergen, Norway. 69 pp. Blumberg, A.F., Mellor, G.L., 1987. A description of a threedimensional coastal ocean circulation model. Pages 1–16 of: Heaps, N. (Ed.), Three-Dimensional Coastal Ocean Models, vol. 4. American Geophysical Union, DC, USA. 208 pp. Cox, M.D., Bryan, K., 1984. A numerical model of the ventilated thermocline. Journal of Physical Oceanography 14, 674 – 687. De Vries, I. (Ed), 1992. Report of the NSTF Modelling Workshop. Rijkswaterstaat, Tidal Waters Division, The Hague. Report DGW-92.045. Delhez, E., Damm, P., De Goede, E., De Kok, J.M., Dumas, F., Gerritsen, H., Jones, J.E., Ozer, J., Pohlmann, T., Rasch, P.S., Skogen, M.D., Proctor, R., 2004. Variability of shelf-seas hydrodynamic models: lessons from the NOMADS2 project. Journal of Marine Systems 45, 39 – 53.
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Egenberg, B. 1993. The relationship between hydrographical variability in coastal water and meteorological and hydrological parameters. M.Phil. thesis, Geophysical Institute, University of Bergen, Norway. 73 pp., in Norwegian. Eide, L.I., Reistad, M., Guddal, J., 1985. Database Av Beregnede Vind Og Bølgeparametre for Nordsjøen, Norskehavet Og Bare˚ rene 1955–81. DNMI, Oslo/Bergen, ntshavet, Hver 6.Time for A Norway. 38 pp., in Norwegian. Gehlen, M., Malschaert, H., Raaphorst, W.R., 1995. Spatial and temporal variability of benthic silica fluxes in the southeastern North Sea. Continental Shelf Research 13, 1675 – 1696. Lenhart, H.-J., 2001. Effects of river nutrient load reduction on the eutrophication of the North Sea, simulated with the ecosystem model ERSEM. Senckenbergiana Maritima 31 (2), 299 – 312. Lohse, L., Malschaert, F.P., Slomp, C.P., Helder, W., Raaphorst, W., 1995. Sediment–water fluxes of inorganic nitrogen compounds along the transport route of organic matter in the North Sea. Ophelia 41, 173 – 197. Lohse, L., Kloostechuis, H.T., Raaphorst, W., Helder, W., 1996. Denitrification rates as measured by the isotope pairing method and by the acetylene inhibition technique in continental shelf sediments of the North Sea. Marine Ecology. Progress Series 132, 169 – 179. Martinsen, E.A., Engedahl, H., 1987. Implementation and testing of a lateral boundary scheme as an open boundary condition in a barotropic ocean model. Coastal Engineering 11, 603 – 627. ˚ dlandsvik, B., Loeng, Martinsen, E.A., Engedahl, H., Ottersen, G., A H., Balin˜o, B. 1992. MetOcean MOdeling Project, Climatological and hydrographical data for hindcast of ocean currents. Tech. rept. 100. The Norwegian Meteorological Institute, Oslo, Norway. 93 pp. Mayer, B. 1995. Ein dreidimensionales, numerisches ScwebstoffTransportmodell mit An-wendung auf die Deutsche Bucht. Tech. rept. GKSS 95/E/59. GKSS-Forschungszentrum Geesthacht GmbH. Moll, A. 1995. Regionale Differenzierung der Prima¨rproduktion in der Nordsee: Unter-suchungen mit einem drei-dimensionale Modell. Tech. rept. 19. Institut fu¨r Meereskunde, Tro-plowitzstr. 7, D-22529 Hamburg. Berichte aus dem Zentrum fu¨r Meeresund Klimaforschung-ReiheB. Moll, A., 1997. Modeling primary production in the North Sea. Oceanography 10 (1), 24 – 26. Moll, A., 1998. Regional distribution of primary production in the North Sea simulated by a three-dimensional model. Journal of Marine Systems 16 (1–2), 151 – 170. Moll, A., 2000. Assessment of three-dimensional physical–biological ECOHAM1 simulations by quantified validation for the North Sea with ICES and ERSEM data. ICES Journal of Marine Science 57 (4), 1060 – 1068. Moll, A., Radach, G., 2001. Synthesis and new conception of North Sea Research (SYCON). Working group 6: review of three dimensional ecological modelling related to the North Sea Shelf system. Berichte aus dem Zentrum fu¨r Meeres- und Klimaforschung-Reihe Z 8, 1 – 225. Moll, A., Radach, G., 2003. Review of three-dimensional ecological modelling related to the North Sea shelf system: Part 1. Models and their results. Progress in Oceanography 57, 175 – 217.
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Oey, L.-Y., Chen, P., 1992. A model simulation of circulation in the Northeast Atlantic shelves and seas. Journal of Geophysical Research 97 (C12), 20087 – 20115. OSPAR, 1998. Report of the ASMO modelling workshop on eutrophication issues 5–8 November 1996, The Hague, The Netherlands. Ottersen, G. 1991. MODgrid, a Model Oriented Data grider. Tech. rept. 6/1991. Institute of Marine Research, Bergen, Norway. 27 pp. Pa¨tsch, J., Radach, G., 1997. Long-term simulation of the eutrophication of the North Sea: temporal development of nutrients, chlorophyll and primary production in comparison to observations. Journal of Sea Research 38, 275 – 310. Pohlmann, T., Puls, W., 1994. Currents and transport in water. In: Su¨ndermann, J. (Ed.), Circulation and Contaminant Fluxes in the North Sea. Springer Verlag, Berlin, pp. 345 – 402. Proctor, R., Baart, A., Berg, P., Boon, J., Deleersnijder, E., Delhez, E., Garreau, P., Gerritsen, H., Jones, J.E., De Kok, J., Lazure, P., Luyten, P., Ozer, J., Pohlmann, T., Ruddick, K., Salden, R., Salomon, J.C., Skogen, M.,Tartinville, B., Vested, H.J. 1997. Final report NOMADS—NOrth sea Model Advection Dispersion Study. Tech. rept. POL Internal 108. Proudman Oceanographic Laboratory. EC concerted action : MAS2-CT94-0105. Proctor, R., Damm, P., Delhez, E., Dumas, F., Gerritsen, H., De Goede, E., Jones, J.E., De Kok, J., Ozer, J., Pohlmann, T., Rasch, P., Skogen, M., Sorensen, J., 2002. North Sea Model Advection Dispersion Study 2: Assessment of model variability. Tech. rept. POL Internal 144. Proudman Oceanographic Laboratory. Final report EU concerted action: MAS3-CT98-0163. Radach, G., Moll, A., submitted for publication. Review of threedimensional ecological modelling related to the North Sea shelf system: Part 2. Model validation and data needs. Progress in Oceanography. Reistad, M., Iden, K.A., 1998. Updating, correction and evaluation of a hindcast data base of air pressure, winds and waves for the North Sea, Norwegian Sea and the Barents Sea. Tech. rept. 9. Det Norske Meteorologiske Institutt, Oslo, Norway. 42 pp. Reistad, M., Eide, L.I., Guddal, J., Magnusson, A.K., 1988. Wave Model Sensitivity Study. The Norwegian Meteorological Institute. Skartveit, A., Olseth, J.A., 1986. Modelling slope irradiance at high latitudes. Solar Energy 36 (4), 333 – 344. Skartveit, A., Olseth, J.A., 1987. A model for the diffuse fraction of hourly global radiation. Solar Energy 37, 271 – 274. Skogen, M.D., Moll, A., 2000. Interannual variability of the North
Sea primary production: comparison from two model studies. Continental Shelf Research 20 (2), 129 – 151. Skogen, M.D., Søiland, H., 1998. A User’s guide to NORWECOM v2.0. The NORWegian ECOlogical Model system. Tech. rept. Fisken og Havet 18/98. Institute of Marine Research, Pb.1870, N-5024 Bergen. 42 pp. Skogen, M.D., Svendsen, E., Berntsen, J., Aksnes, D., Ulvestad, K.B., 1995. Modelling the primary production in the North Sea using a coupled 3-dimensional physical chemical biological ocean model. Estuarine, Coastal and Shelf Science 41, 545 – 565. Skogen, M.D., Svendsen, E., Ostrowski, M., 1997. Quantifying volume transports during SKAGEX with the Norwegian ecological model system. Continental Shelf Research 17 (15), 1817 – 1837. Skogen, M.D., Søiland, H., Svendsen, E., 2004. Effects of changing nutrient loads to the North Sea. Journal of Marine Systems 46 (1–4), 23 – 38. Smith, J., Damm, P.E., Skogen, M.D., Flather, R.A., Pa¨tsch, J., 1996. An investigation into the variability of circulation and transport of the North–West European shelf using three hydrodynamic models. Deutsche Hydrographische Zeitschrift 48 (3/4), 325 – 348. Søiland, H., Skogen, M.D., 2000. Validation of a 3-D biophysical model using nutrient observations in the North Sea. ICES Journal of Marine Science 57 (4), 816 – 823. Stigebrandt, A., 1980. Barotropic and baroclinic response of a semienclosed basin to barotropic forcing of the sea. In: Freeland, H.J., Farmer, D.M., Levings, C.D. (Eds.), Proceeding of the NATO Conference on Fjord Oceanography. Plenum Press, New York, pp. 141 – 164. ˚ dlandsvik, B., MarSvendsen, E., Berntsen, J., Skogen, M.D., A tinsen, E., 1996. Model simulation of the Skagerrak circulation and hydrography during SKAGEX. Journal of Marine Systems 8 (3–4), 219 – 236. SWAMP-Group,, 1985. Ocean Wave Modelling. Plenum Press, New York. 256 pp. Wei, H., Jun, S., Moll, A., Zhao, L., 2004. Phytoplankton dynamics in the Bohai Sea—observations and modeling. Journal of Marine Systems 44 (3–4), 233 – 251. Weichart, G., 1980. Chemical changes and primary production on the Fladen Ground area during the first phase of spring phytoplankton bloom. Meteor-Forschungsergebnisse. Reihe A 22, 79 – 86.
Importance of ocean circulation in ecological modeling: An example from the North Sea Morten D. Skogen a,*, Andreas Moll b b
a Institute of Marine Research, Pb. 1870, N-5817 Bergen, Norway Institut fu¨r Meereskunde, Universita¨t Hamburg, Bundesstr. 53, D-20146 Hamburg, Germany
Received 11 April 2005; received in revised form 16 June 2005; accepted 16 June 2005 Available online 24 August 2005
Abstract There is an increasing number of ecological models for the North Sea around. Skogen and Moll (2000) [Skogen, M.D., Moll, A. 2000. Interannual variability of the North Sea primary production: comparison from two model studies. Continental Shelf Research 20 (2), 129–151] compared the interannual variability of the North Sea primary production using two state-ofthe-art ecological models, NORWECOM and ECOHAM1. Their conclusion was that the two models agreed on an annual mean primary production, its variability and the timing and size of the peak production. On the other hand, there was a low (even negative dependent of area) correlation in the production in different years between the two models. In the present work, these conclusions are brought further. To try to better understand the observed differences between the two models, the two ecological models are run in an identical physical setting. With such a set-up also the interannual variability between the two models is in agreement, and it is concluded that the single most important factor for a reliable modeling of phytoplankton and nutrient distributions and transports within the North Sea is a proper physical model. D 2005 Elsevier B.V. All rights reserved. Keywords: Primary production; North Sea; Ecological model
1. Introduction There is an increasing number of ecological models for the North Sea. Moll and Radach (2003) provided an overview especially about three-dimensional models that describe and predict how the marine ecosystem of the greater North Sea area functions and how concentrations and fluxes of biologically * Corresponding author. E-mail address: [email protected] (M.D. Skogen). 0924-7963/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2005.06.002
important elements vary in space and time, throughout the shelf and over years, in response to physical forcing. The models differ both in the complexity of the biochemical cycles included, and how the ocean circulation to force the biology is modeled. They are all validated (OSPAR, 1998; Radach and Moll, 2005), believed to different levels of confidence to reproduce the North Sea conditions (OSPAR, 1998; Moll and Radach, 2001), and are also used for management purposes, e.g. to investigate the effects of changes in river nutrient loads (Pa¨tsch and Radach, 1997; Len-
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of the North Sea has a long history, and a lot of data are available, it has proved difficult (Delhez et al., 2004) to conclude one model better than another in general. All models seem to have both weaknesses and strengths. For ecological models, the situations are even more difficult. There have been a few attempts to compare such models (de Vries, 1992; OSPAR, 1998). All models meet general assumptions on, e.g. the mean North Sea primary production, but the differences in both
hart, 2001; Skogen et al., 2004). From the comparison between the models of the greater North Sea and observations, it has become clear that ecosystem models should be three-dimensional and should be coupled with or forced by state-of-the-art circulation models (Moll and Radach, 2003). There have been several big projects to compare and validate different North Sea circulation models (e.g. Proctor et al., 1997, 2002). Even if physical modeling
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their state variables and area covered have made a proper comparison an almost impossible task. Skogen and Moll (2000) compared the interannual variability of the North Sea primary production using two state-of-the-art ecological models, NORWECOM (Skogen et al., 1995) and ECOHAM1 (Moll, 1995, 1998; Wei et al., 2004). The conclusion in that paper was that the two models agreed on an annual mean primary production, its variability and the timing and size of the peak production. Also the integrated influence of the river inputs was equal, even if some spatial differences were apparent. The interannual variability could in both models to a large extent be explained from differences in the physical conditions between the years. On the other hand, the physical process that triggered the differences in the primary production between the years was not the same, and there was a low (even negative dependent of area) correlation in the production in different years between the two models. From these results, it was stated that changes in the physical conditions and forcing resulted in a large variability in the primary production in the North Sea, and that a proper circulation model, including both a realistic horizontal advection, exchange with the Atlantic, and a proper simulation of the vertical density structures was essential for primary production studies in that area. In the present work, these thoughts are brought further. To try to better understand the observed differences between the two models, the two ecological models are run in an identical physical setting. This is done by including the biochemical part of ECOHAM1 in the physical setting of NORWECOM, such that the resulting model have two ecological options with the same underlying physical model (see Fig. 1). With such a setting, it should be possible to identify to which order the observed differences in Skogen and Moll (2000) was due to the different physical models, or the differences in the biochemical cycle. Also, such an exercise can show the importance of a proper physical model to force ecological models. This is of special importance when used for management purposes. In the present study, both models have been run for 10 different years (1985–1994), with identical ocean physics, river inputs and light. To isolate the direct effects of interannual variability in the ocean physics to the ecological models, both river inputs and light are fixed to 1990 values in all simulations, whereas
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only surface pressure and wind forcing remain variable. It should be noted that in all development and tuning of ecological models, there is a dependency between the physical model and the biological parameter settings. When using ECOHAM1 in this new completely different physical setting (it is normally forced with daily mean currents, but in this work it is directly coupled to a tidal resolving physical model), too low primary production results appeared. One parameter, phytoplankton mortality rate, was tuned to get proper mean annual North Sea primary production compared to NORWECOM. There are still some difficulties due to low phytoplankton values in the English Channel in the ECOHAM1 results which we ignored in the present work, but no further parameter tuning have appeared since we treat this study as a comparison, not a validation study.
2. The models The study has focused on the years 1985–1994. The first simulation started on December 15, 1984, and the models have been run one by one year with re-initialization every December 15. The models have been run on the same grid with identical forcing, initial fields and open boundaries. To ease the analysis, the monthly mean river loads, and the annual light cycles were identical for each year. One can argue that a re-initialization every December 15 means that the models are not in proper dynamic balance, and that the models might give very different scenario effects in cases where multiyear scenario runs are needed due to the timescale of the changes in the model domain (e.g. to include effects from the sediment pools). Both models can be used for multi-year simulations, but to be consistent with the set-up in Skogen and Moll (2000), the reinitializations are kept. In addition, the main focus in the present study is on interannual variability and effects from changed circulation. Like the use of identical annual cycles of light and river loads, the re-initialization highlights this effect. 2.1. The physical model The two physical models used in Skogen and Moll (2000) has been compared in a long-term modeling
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The model is one out of a set of four marine biogeochemistry and ecosystem community models hosted at the Max-Planck-Institut fu¨r Meteorologie in Hamburg to provide central support for the German and European climate research community and can be downloaded (http://www.mad.zmaw.de/). The aim was to quantify the annual primary production under actual circulation and solar radiation forcing (Moll, 1998) and to describe concentrations and fluxes of biologically important elements in space and time including a full validation (Moll, 2000). Based on ECOHAM1 (Moll, 1998), dissolved inorganic nitrogen (DIN) as a new state variable and the biogeochemical processes concerning the nitrogen cycle were added (Wei et al., 2004). The new ECOHAM1 including DIN was than applied to the Bohai Sea with very little changes in the model parameters. A schematic diagram of the state variables and processes is shown in Fig. 1. Three partial differential equations describe the spatio-temporal evolution in the pelagic realm for phosphate, DIN and phytoplankton. Phytoplankton is represented by one state variable and the model formulations are based on phosphorus and nitrogen to limit the phytoplankton production. Grazing of phytoplankton by zooplankton is treated dynamically due to a formulation according to Michaelis–Menten including a threshold value below phytoplankton grazing ceases. The model is
study of variability, circulation and transports in the North Sea (Smith et al., 1996). This study concluded that the models showed similar patterns of variability in volume transports, that the mean values compared well in the southern North Sea, but that the NORWECOM physics had somewhat higher transports in the northern North Sea. For the present study, it was chosen to use the physical module from the NORWECOM model to run both models using the same physics. That physical model is based on the threedimensional, primitive equation, time-dependent, wind- and density-driven Princeton Ocean Model (POM). The model is fully described in Blumberg and Mellor (1987). In the present study, the model is used with a horizontal resolution of 20 20 km2 on an extended North Sea (see Fig. 2). In the vertical, 12 bottom following sigma layers are used. 2.2. The ECOHAM1 model The ECOlogical North Sea Model, HAMburg, Version 1 (ECOHAM1) is a three-dimensional model system that study nutrient and phytoplankton dynamics. It is the robust German ecosystem model of the North Sea that has been developed at the Institute fu¨r Meereskunde, Hamburg, first, for the simple phosphorus cycle (Moll, 1997), and later extended for an additional simple nitrogen cycle by Wei et al. (2004).
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conceptualized for a shelf sea including the shallow sea characteristic for the replenishment of the water column with nutrients from the bottom. Therefore, two ordinary differential equations describe the benthic detritus in terms of phosphorus and nitrogen pools. Underwater light is calculated by a diagnostic ordinary differential equation that includes shelf shading due to phytoplankton.
data from DNMI’s operational wave model, WINCH (SWAMP-Group, 1985; Reistad et al., 1988), are used. Parameterization of the biochemical processes is taken from literature based on experiments in laboratories and mesocosms, or deduced from field measurements (Aksnes et al., 1995; Pohlmann and Puls, 1994; Mayer, 1995; Gehlen et al., 1995; Lohse et al., 1995, 1996).
2.3. The NORWECOM model
2.4. Model set-up, forcing and initialization
The NORWegian ECOlogical Model system (NORWECOM) is a coupled physical, chemical, biological model system (Aksnes et al., 1995; Skogen et al., 1995; Skogen and Søiland, 1998) applied to study primary production, nutrient budgets and dispersion of particles such as fish larvae and pollution. The model has been validated by comparison with field data in the North Sea/Skagerrak in, e.g. Svendsen et al. (1996), Skogen et al. (1997, 2004) and Søiland and Skogen (2000). 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), phosphorous (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) and oxygen (see Fig. 1). The processes included are primary production, respiration, algae death, remineralization of inorganic nutrients from dead organic matter, self shading, turbidity, sedimentation, resuspension, sedimental burial and denitrification. Phytoplankton mortality is given as a constant fraction, and is assumed to account also for zooplankton grazing which in this context is included as a forcing function. Particulate matter has a sinking speed relative to the water and may 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. Initial and forcing data on suspended particulate matter, are taken from Pohlmann and Puls (1994). Remineralization takes place both in the water column and in the sediments. The bottom stress is due to both currents (including tides) and surface waves. To calculate the wave component of the bottom stress,
The forcing variables for the physical model are 6hourly hindcast atmospheric pressure fields provided by the Norwegian Meteorological Institute (DNMI) (Eide et al., 1985; Reistad and Iden, 1998), 6-hourly wind stress (translated from the pressure fields by assuming neutral air–sea stability), four tidal constituents and freshwater runoff. In the lack of proper data on the surface heat fluxes, a Qrelaxation towards climatologyQ method is used (Cox and Bryan, 1984). During calm wind conditions, the surface temperature field will adjust to the climatological values after about 10 days (Oey and Chen, 1992). The net evaporation precipitation flux is set to zero. Initial values for velocities, water elevation, temperature and salinity are taken from monthly climatologies (Martinsen et al., 1992). Interpolation between monthly fields are also used at all open boundaries, except at the inflow from the Baltic where the volume fluxes have been calculated (Stigebrandt, 1980) from the modeled water elevation in Kattegat and the climatological monthly mean freshwater runoff to the Baltic. To absorb inconsistencies between the forced boundary conditions and the model results, a 7-gridcell QFlow Relaxation SchemeQ (FRS) zone (Martinsen and Engedahl, 1987) is used around the open boundaries. The incident irradiation is modeled using a formulation based on Skartveit and Olseth (1986, 1987). Data for global daily radiation from 1990 is taken from a station at Taastrup (Denmark) (Anon., 1991) and used for all years. Nutrients (inorganic nitrogen, phosphorus and silicate) are added to the system from the rivers and from the atmosphere (only inorganic nitrogen). River data (freshwater and nutrient loads) from 1990 are used for all years. These data originates from Rijkswaterstaat (Belgium and the Netherlands), Arbeitsgemeinschaft fu¨r die Reinhaltung der Elbe and
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¨ kologie (GerNiedersa¨chsisches Landesamt fu¨r O many), National Environmental Research Institute (Denmark), The Swedish Meteorological and Hydrological Institute and Swedish University of Agriculture (Sweden) and the Norwegian Water Resources and Energy Directorate and the Norwegian State Pollution Control Authority (Norway). In the lack of data from British rivers, climatological data were used (Balin˜o, 1993). In addition, some extra freshwater is added along the Norwegian and Swedish coast to fulfill requirements to estimated total freshwater runoff from these coastlines (Egenberg, 1993). The initial nutrient fields are derived and extrapolated/interpolated (Ottersen, 1991) from data (obtained from ICES) together with some small initial amounts of algae (NORWECOM 0.10 mg N m 3 for diatoms and flagellates, ECOHAM1 0.20 mg N m 3 for phytoplankton). Nutrient data (monthly means) measured in the Baltic (ICES) are used for the water flowing into Kattegat.
3. Results The horizontal distribution of the integrated annual primary production have been calculated, and in Fig. 3 the 10 years annual mean production for both models are given. In addition, the mean production in each ERSEM box (see Fig. 4) and the integrated
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mean North Sea (boxes 1–10) primary production have been found for each year. All numbers are given in Table 1 (NORWECOM) and in Table 2 (ECOHAM1). Also the standard deviation, referring to the spatial variability of the mean of each box, is given. The last column gives the span: (prodmax prodmin) / prodmean 100% for each box. This number is used as a measure of the interannual variability of the production. When discussing the results we recall the model names for the biochemical setup of state variables as described in Fig. 1.
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Fig. 4. The different ERSEM boxes (1–10).
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Fig. 3. Mean annual production (gC/m2/year) from the NORWECOM model (left) and the ECOHAM1 model (right).
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Table 1 Production statistics for ERSEM boxes with the NORWECOM model Box/year
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113 105 113 84 106 157 136 171 224 144 122
122 108 136 94 124 138 140 169 203 154 129
101 89 142 70 116 132 136 176 208 160 120
116 117 121 83 118 181 165 179 170 135 128
114 98 133 69 102 144 157 191 193 119 120
98 101 128 85 105 158 145 177 184 129 121
101 85 128 63 116 136 146 180 210 129 116
109 101 118 70 105 137 132 179 224 144 118
98 94 139 66 140 127 149 185 194 137 122
99 91 122 65 117 129 137 173 189 131 114
107 99 128 75 115 144 144 178 200 138 121
22 20 22 8 25 45 36 43 39 39 45
22 32 23 41 33 38 23 12 27 30 12
Annual production (gC/m2 /year), mean production, standard deviation and the span ((max min) / mean 100%). All numbers (except the span) are point-wise means covering all grid points within a box. The standard deviation is referencing to the spatial variation of the mean production within a box, and the span to the interannual variability of the annual means of a box.
The results using the NORWECOM model varies from 63 gC/m2/year to 224 gC/m2 /year (box averages from Table 1) within the North Sea. The highest numbers are found close to the main rivers along the continental coast in the southern North Sea. In the central North Sea, the production is generally much lower, while the production is increasing northward due to the inflow of nutrient-rich Atlantic water. Separating the production between diatoms and flagellates, there is a north–south gradient in the flagellate production, while there is a more uniform diatom distribution. The highest relative interannual variability in the production is found in box 4 (Central North Sea) with a span of 41%, but there are several boxes with a span of more than 30%. Except for the minimum found in box 8 (Belgian/Dutch coast) (12%), all boxes are reporting on a span of at least 20%. The reason for the low span in box 8 is due to the strong influence of rivers, and that these inputs are fixed in all runs. On the other hand, the same box (8) has a large number for the standard deviation (43 gC/m2/year), since the influence of the river nutrients is highly variable within the box. The smallest standard deviation is found in the Central North Sea (box 4). Focusing on the mean North Sea production the variability is much lower. The span is only 12%, equal that found in box 8. This indicates that low production in one area, is compensated with higher production elsewhere, and that it is difficult to distinguish between years with high and low productivity in the
North Sea only due to changes in the wind-driven circulation. An example of such a compensation of production is between the central North Sea boxes (4 and 5) in 1993. This year box 4 has a very low production, while its neighboring box (5) has its overall maximum. This can probably be explained from a small shift in the modeled circulation. Investigating the seasonal patterns, they are similar for all years with a maximum production in May of around 33 gC/m2/month as a mean for the whole North Sea. This is the same production rate as reported on by Weichart (1980) for the FLEX study in the northern North Sea in the spring of 1976. Between the end of April and the beginning of June, the production was about 30 gC/m2. Comparing the different months between the years, there are only small differences (10–15%). The exception for this is June 1986 and August 1988 when the production is about 30% higher than the mean June and August production, respectively. This increased production is due to a very high inflow of nutrient-rich Atlantic water as nutrients are depleted after the late spring bloom. For the ECOHAM1 model, primary production varies from 86 gC/m2/year to 243 gC/m2/year within the North Sea (box averages from Table 2). The highest numbers are found in the German Bight (box 9), while the lowest production is in the Central North Sea (box 4). The highest interannual variability in the production is found along the Belgian and Dutch coast (box 8) with a span of 50%.
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Table 2 Production statistics for ERSEM boxes with the ECOHAM1 model Box
85
86
87
88
89
90
91
92
93
94
Mean
S.D.
Span
1 2 3 4 5 6 7 8 9 10 Mean
105 119 135 95 123 128 89 104 221 183 120
111 115 160 113 124 108 86 77 216 179 123
88 101 157 85 124 119 87 102 243 187 117
118 131 142 103 116 147 121 113 163 167 125
106 110 144 90 119 117 102 131 207 159 120
92 113 147 112 125 141 111 131 190 161 126
94 105 142 92 127 126 109 126 215 158 120
98 113 152 87 125 108 86 113 221 187 119
101 111 155 80 151 113 97 99 206 164 120
105 114 132 90 139 120 85 88 221 171 118
102 113 147 95 127 123 97 108 210 172 121
9 18 25 12 26 44 38 58 57 53 43
29 27 19 35 28 32 37 50 38 17 7
Annual production (gC/m2 /year), mean production, standard deviation and the span ((max min) / mean 100%). All numbers (except the span) are point-wise means covering all grid points within a box. The standard deviation is referencing to the spatial variation of the mean production in a box, and the span to the interannual variability of the annual means of a box.
Outputs from the two models have many similarities with low production in the central and northern North Sea, with increasing production towards the coasts. The mean annual North Sea production is 121 gC/m2/year with both models. Except for the Belgian and Dutch coast (box 8), the interannual variability using ECOHAM1 are in the same range as for NORWECOM. Also for the spatial difference within the boxes (as given by the standard deviation), all numbers from ECOHAM1 are comparable to those from NORWECOM except for somewhat higher values in the coastal boxes (8, 9 and 10). Also the correlation in production between different years for the two models are generally high (see Table 3). In five of the boxes (2, 4, 6, 7 and 10) r N 0.80, and only in one box (8) it is below 0.70. However, this box is exceptional as will be discussed below. Taking the mean over the whole North Sea has the lowest correlation (r = 0.63), but when removing the box 8 from the calculation the correlation is r = 0.81. Apparently, there is a shift in the timing of the peak (monthly) primary production with ECOHAM1 compared to NORWECOM. In ECOHAM1, the maximum production is found in June, except for 1988
when the peak was in May. However, focusing on daily production rates, the peak is almost identical in both models, with a maximum around June 1. The monthly shift is therefore due to differences in the shape of the curve of daily production. Except for the monthly production peak shift in 1988, there are less interannual differences between the months using ECOHAM1, but also with ECOHAM1 June 1986 and August 1988 has the highest monthly production rates. The largest differences between the models are seen in the English Channel and along the Belgian and Dutch coast (box 8). In these areas, ECOHAM1 have very low production, despite the high loads of river nutrients. The reason for this has been examined, and can be explained from the differences in maximum production rate between the two models. In ECOHAM1, this rate is 1.5 (day 1), while in NORWECOM it is temperature dependent with a value of 2.4 at 168. The lower value gives a downstream shift in the production. By using ECOHAM1 in its normal physical setting, these low productions are not seen, and by increasing the maximum production value to that used in NORWECOM in the present setting, a similar pattern to that for NORWECOM results. How-
Table 3 Correlation in production between different years for ECOHAM1 and NORWECOM Box
1
2
3
4
5
6
7
8
9
10
Mean
0.73
0.88
0.71
0.86
0.70
0.83
0.84
0.64
0.75
0.85
0.63
130 NORWECOM ECOHAM1
128 9
126
annual mean production
200
ECOHAM1
10
3
150 5
6
2 1 100
4
8
124 122 120 118 116
7 114 112
50 50
100
150 NORWECOM
200
250
110 1984
1985
1986
1987
1988
1989 1990 Year
1991
1992
1993
1994
1995
Fig. 5. Comparison of mean annual production (gC/m2/year) for the NORWECOM versus ECOHAM1 model (left) in the space domain using the regional annual production for the defined 10 different boxes of Fig. 4 including the standard deviation of the 10 years, and (right) in time domain presenting the 10 years time series of the mean North Sea production.
M.D. Skogen, A. Moll / Journal of Marine Systems 57 (2005) 289–300
250
297
298
M.D. Skogen, A. Moll / Journal of Marine Systems 57 (2005) 289–300
Table 4 Correlation in production between different years for ECOHAM1 with NP and P limitations Box
1
2
3
4
5
6
7
8
9
10
Mean
0.99
0.99
0.98
0.99
0.91
0.99
0.96
1.00
1.00
0.90
0.97
ever, as this is not a validation study but a model sensitivity study, no further tuning of parameters have been done. In general, the production using NORWECOM is higher in the southern North Sea and along the British coast (boxes 6, 7 and 8) than by using ECOHAM1 (Fig. 5, left panel), and there is a better agreement between the two models in the first half of the period (1985–1989) than for the second part (Fig. 5, right panel).
4. Discussion One of the main applications of ecological ocean models are to be used for management purposes, such as scenario simulations, e.g. examining the effect of changes in anthropogenic forcing like river nutrient inputs. Also, the effect of a possible climate change to the marine environment is of vital interest, and will to a large extent depend on reliable models and their predictions. However, before a model can be used for such studies it must be properly validated and able to reproduce today’s conditions. An important aspect of this is the interannual variability. Even if two models agree on the mean conditions and patterns, they are likely to give quite different answers to management efforts and the effect of climate change if the interannual signals due to changes in the atmospheric forcing is not consistent. Even if the two models (NORWECOM and ECOHAM1) to a large extent agreed in a previous study (Skogen and Moll, 2000), their disagreement in the interannual variability will introduce large uncertainties when either of them is used for predictions of the effect of changed forcing. It is therefore essential to understand their different behavior. The present study
shows that to first order the differences in ocean circulation (that forces the biological models) can explain the observed disagreement in interannual variability, and that the different biological formulations are of less importance when focusing on the changes in primary production. This conclusion can be further emphasized with two additional experiments. In the first one, a simplification of the NORWECOM model is done, such that it is run with only one phytoplankton (flagellates) and two nutrients (inorganic nitrogen and phosphorous), and in the second one the ECOHAM1 model is run with phosphorous limitation only. In the ECOHAM1 results, there is an increase in the mean North Sea production of 16% when only Plimitation is performed. The increase is largest in the northern North Sea, telling that N-limitation is more likely to occur in these areas, while there are no change in the German Bight (box 9). However, despite the changed level of production, the interannual variability is almost undisturbed with the two different versions of ECOHAM1. This is shown in Table 4, where most correlations are found to be very close to 1.0. There is a decrease in the total production between the two versions of the NORWECOM model of more than 15%. The decrease is highest in the northern North Sea, where the diatom production in the full model was highest. However, when comparing the flagellate production only, there is an increase of about 40%. This increase has an almost uniform distribution. Nevertheless, despite the changed level of production, the interannual variability is almost undisturbed with the two versions of the NORWECOM model (see Table 5) like it was with the ECOHAM1 model. From all these simulations, we conclude that the single most important factor for a reliable modeling of
Table 5 Correlation in production between different years for NORWECOM with one and two phytoplankton state variables Box
1
2
3
4
5
6
7
8
9
10
Mean
0.89
0.95
0.83
0.96
0.86
0.99
0.95
0.96
0.97
0.98
0.97
M.D. Skogen, A. Moll / Journal of Marine Systems 57 (2005) 289–300
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