QUALITY INFORMATION DOCUMENT
For IBI Biogeochemical Analysis Product
IBI_REANALYSIS_BIO_005_003
Issue: 2.1
Contributors: Dabrowski T., Reffray G., Perruche C., Gutknecht E.
Approval Date by Quality Assurance Review Group : under review
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CHANGE RECORD
Issue
Date
§
Description of Change
Author
Validated By
1.0
12/05/2014
All
Creation of the document
Reffray, G.
Enrique Álvarez Fanjul
1.1
11/03/2015
All
Revision by QuARG after V5 QuARG
acceptance
QuARG
1.2
May 1 2015
all
Change format to fit CMEMS
graphical rules
L. Crosnier
2.0
14/12/2015
all
Inclusion of new metrics to T. Dabrowski, G.
Enrique Álvarez Fanjul
validate the IBI BIO REA Reffray, C. Perruche, E.
update at CMEMS V2
Gutknecht, M.G. Sotillo
2.1
16/03/2016
All
Expansion of descriptive text T. Dabrowski, G.
following the Design Review in Reffray, C. Perruche, E.
February 2016
Gutknecht, M.G. Sotillo
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TABLE OF CONTENTS
I
EXECUTIVE SUMMARY ............................................................................................................................ 4
I.1 Products covered by this document ........................................................................................................... 4
I.2 Summary of the results ............................................................................................................................... 5
I.3 Estimated Accuracy Numbers .................................................................................................................... 6
II
Production Subsystem description ................................................................................................................ 9
II.1 Physical model NEMO .............................................................................................................................. 9
II.2 Biogeochemical model PISCES ................................................................................................................ 9
II.3 Coupling and configuration .................................................................................................................... 10
III
Validation framework ............................................................................................................................. 11
IV
Validation results ......................................................................................................................................... 13
IV.1 Chlorophyll ............................................................................................................................................. 13
IV.2 Nitrates .................................................................................................................................................... 19
IV.3 Phosphate ................................................................................................................................................ 25
IV.4 Silicate ..................................................................................................................................................... 30
IV.5 Oxygen..................................................................................................................................................... 35
IV.6 Ammonium ............................................................................................................................................. 40
IV.7 Iron .......................................................................................................................................................... 42
IV.8 Phytoplankton biomass in carbon ......................................................................................................... 44
IV.9 Primary production ................................................................................................................................ 46
IV.10 Euphotic layer depth ............................................................................................................................ 53
V
REferences ................................................................................................................................................... 57
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EXECUTIVE SUMMARY
I.1 Products covered by this document
The product assessed in this document is referenced as: IBI_REANALYSIS_BIO_005_003
IBI_REANALYSIS_BIO_005_003 product includes 3D monthly averages of different
biogeochemical variables (i.e.: Nitrates, Phosphates, Ammonium, Iron, Silicate, Euphotic depth,
Dissolved Oxygen, Concentration of Chlorophyll, Phytoplankton Biomass and Primary Productivity),
covering the period 02/2002 – 12/2014.
The
The product is delivered through two different datasets: dataset-ibi-reanalysis-bio-005-003-monthlyregulargrid and dataset-ibi-reanalysis-bio-005-003-monthly-nativegrid. Both datasets disseminate
same information. The former provides the information already post-processed into a regular lat/lon
grid (analogous to the one used to deliver the physical reanalysis and the IBI Near-Real-Time Forecast
Product), whereas the second one provides the info in the original model native curvilinear ORCA
grid. Both datasets provide the IBI multiyear information at 1/12° resolution (IBIRYS: IBI Reanalysis),
covering the same geographical domain: the IBI Service Domain (detail in pink in Figure 1). This IBI
Service domain covers the area where the IBI MFC has responsibility (within the CMEMS regional
framework) of delivering forecast and multi-year info. However, the biogeochemical model
application used to generate the output data used to generate the CMEMS IBI multi-year
biogeochemical product extends further (detailed in black in Figure 1). The IBI MFC Team in charge
of the production and the later validation of the products works on this extended domain. Thus,
maps presented in this document show the full extent of the IBI model domain, whereas all statistics,
metrics and the EANs, Estimated Accuracy Numbers, end-user oriented were computed for the IBI
service Domain , where info is available to CMEMS users.
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Figure 1: Extents of the IBI Service Domain used to deliver IBI multi-year biogeochemical products
to CMEMS user (magenta) and the full native model domain, used to run the IBIRYS simulation
(black).
I.2 Summary of the results
The quality of the biogeochemical product delivered has been assessed using a 12-year time period
(years 2003-2014). The headline results for each of the variables assessed are as follows:
Chlorophyll: At sea surface, modelled chlorophyll fields show a good agreement with satellite data
and show a significant improvement confronted to the global model in particular along the coasts,
the Mediterranean Sea and the English Channel. However, concentrations are slightly lower (mean
bias defined as data-model = 0.037 mgm-3). Markedly, the Rhône plume has higher than observed
concentrations. Concerning the temporal variation, the model manages well to reproduce the
seasonal cycle (spring bloom) with a good timing while the global ocean model exhibits a lag time or
1 or 2 months. The amplitude of the seasonal cycle is too strong: the model overestimates the peak
bloom and chlorophyll concentration drops too quickly and remains below the observed values
during summer and autumn (explaining the mean bias between data and model). Based on the
Percentage of Bias, the model possesses good skill most of the time and is equally often very good
and excellent.
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Nitrates: The concentration of nitrates is too high at sea surface, with a bias of -0.312 µmol/L. In the
northern parts of the domain they are overestimated, whereas in the southern part they are
underestimated. It should be noted that the model performance deteriorates markedly in the
summer months when compared to the remaining months and the model generally overestimates
nitrates in the summer.
Phosphates: The concentration of phosphates is slightly too low at sea surface, with a bias of only
0.024 µmol/L. This is mainly due to the underestimation in the model between the months
September – December. Overall the model displays similar spatial pattern to the World Ocean Atlas
Climatology over the IBI region, however, similarly to nitrates it overestimates concentrations at
higher latitudes and underestimates at lower latitudes.
Silicates: The concentration of silicates is too low at sea surface over the IBI area, with a bias of 0.380
µmol/L, mainly due to the underestimation between November and March. Overall, it displays
similar spatial pattern to the World Ocean Atlas Climatology, however, underestimation along the
African coast, around the Iberian Peninsula and in the Bay of Biscay is clearly pronounced.
Dissolved oxygen: Oxygen presents a very good agreement at sea surface in comparison with World
Ocean Atlas climatology, with a bias of -3.168 µmol/L (just over 1% error). This is due to the intrinsic
link between O2 concentration and temperature (and especially at sea surface). Dissolved oxygen
benefits from data assimilation via temperature. At subsurface, the model is able to reproduce the
oxygen minimum in the tropical band (longitudinal transect), also extending north and visible in the
latitudinal transects.
Euphotic depth: Euphotic depth is represented very well in the model compared to the product
derived from the satellites (Globcolour). Over the IBI domain, the model overestimates euphotic
depth by 2.96 m, which represents approximately 5% bias. Based on the Percentage of Bias the
model possesses mostly excellent and very good skill on the monthly time scale. In general, the
spatial pattern is represented well, with the exception of the north-west of the domain, where model
overestimates the euphotic depth and the Mediterranean Sea, where it is underestimated.
Primary productivity: Vertically integrated primary productivity (NPP) in the model is significantly
lower when compared to the satellite derived product (the VGPM model) and the bias stands at 565
mgC m-2 d-1. Based on the Percentage of Bias the model skill is assessed as poor / bad. Significant
underestimation of primary productivity along the European Atlantic coasts compared to VGPM has
previously been reported for other models (e.g. Schourup-Kristensen et al., 2012). As well Campbell
et al. (2002), comparing primary production models (such as VGPM) and in-situ measurements of
NPP, reported that “best performing algorithm agree with in-situ estimates within a factor 2”. So
comparisons have to be taken with caution. Instead, normalized NPP are very similar, and seasonal
phasing is generally good, with however a peak productivity about 1 month ahead of VGPM in most
years.
I.3 Estimated Accuracy Numbers
A consistent estimation of the relevant accuracy levels for the IBI-MFC multi-year biogeochemical
product, currently delivered through the CMEMS catalogue, has been performed. This section is
devoted to provide essential statistics on the delivered biogeochemical variables, obtained from
long-term comparisons with reference information based on both satellite and climatology data
sources.
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Table 1: Mean and standard deviation values over the IBI Service domain at sea surface computed
for model and observation averaged over the years 2003-2014 (satellite or climatology).
Variable
Observations
Model
Chlorophyll
Mean = 0.6093 mg m-3
Mean = 0.5720 mg m-3
Std = 0.7714 mg m-3
Std = 0.6286 mg m-3
Mean = 251.482 µmol L-1
Mean = 254.651 µmol L-1
Std = 15.152 µmol L-1
Std = 15.933 µmol L-1
Mean = 2.725 µmol L-1
Mean = 3.036 µmol L-1
Std = 2.105 µmol L-1
Std = 2.839 µmol L-1
Mean = 0.236 µmol L-1
Mean = 0.212 µmol L-1
Std = 0.137 µmol L-1
Std = 0.186 µmol L-1
Mean = 1.927 µmol L-1
Mean = 1.547 µmol L-1
Std = 0.687 µmol L-1
Std = 0.861 µmol L-1
Mean = 56.88 m
Mean = 59.84 m
Std = 17.63 m
Std = 17.72 m
Mean = 876.5 mgC m-2 d-1
Mean = 311.4 mgC m-2 d-1
Std = 542.6 mgC m-2 d-1
Std = 115.5 mgC m-2 d-1
Dissolved oxygen
Nitrates
Phosphates
Silicates
Euphotic depth
Net primary
productivity
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Table 2: Root Mean Square Error
(mod - obs)2
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, Mean Error <(obs – mod)>, Percentage of
Bias (%) = |Mean Error /Mean Obs|, and Correlation is R – coefficient of correlation, calculated at
sea surface with the fields averaged over the years 2003-2014.
Variable
RMS difference
Mean Error
Percent Bias (%)
Correlation
Chlorophyll
0.4807 mg m-3
0.0373 mg m-3
6.12
0.78
Dissolved Oxygen
3.964 µmol L-1
-3.168 µmol L-1
1.26
0.99
Nitrates
1.247 µmol L-1
-0.312 µmol L-1
11.44
0.92
Phosphates
0.078 µmol L-1
0.024 µmol L-1
10.05
0.94
Silicates
0.785 µmol L-1
0.380 µmol L-1
19.72
0.63
Euphotic depth
8.43 m
-2.96 m
5.20
0.90
Net primary
productivity
727.1 mgC m-2 d-1
565.1 mgC m-2 d-1
64.47
0.79
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PRODUCTION SUBSYSTEM DESCRIPTION
Production Center: IBI MFC
Production Unit: Mercator Ocean
Dissemination Unit: Puertos del Estado
Scientific Validation Expertise: Marine Institute
II.1 Physical model NEMO
The physical ocean model and its main validation results are described in the Products User Manual
Document CMEMS-IBI-PUM-005-002-v2.0 (Sotillo et al, 2016) and the Quality Information Document
CMEMS-IBI-QUID-005-002-v2.0 (Levier et al., 2016) dedicated to the IBI_REANALYSIS_PHY_005_002
product. Further information on the IBI reanalysis system can be found in Sotillo et al 2015.
II.2 Biogeochemical model PISCES
The biogeochemical model used is PISCES (Aumont and Bopp, 2006) in its version NEMO3.2. It is a
model of intermediate complexity designed for global ocean and is part of NEMO modeling platform.
It has 24 prognostic variables and simulates biogeochemical cycles of oxygen, carbon and the main
nutrients controlling phytoplankton growth (nitrate, ammonium, phosphate, silicate and iron). The
model distinguishes four plankton functional types based on size: two phytoplankton groups (small =
nanophytoplankton and large = diatoms) and two zooplankton groups (small = microzooplankton and
large = mesozooplankton). Prognostic variables of phytoplankton are total biomass in C, Fe, Si (for
diatoms) and chlorophyll and hence the Fe/C, Si/C, Chl/C ratios are variable. For zooplankton, all
these ratios are constant and total biomass in C is the only prognostic variable. The bacterial pool is
not modeled explicitly. PISCES distinguishes three non-living pools for organic carbon: small
particulate organic carbon, big particulate organic carbon and semi-labile dissolved organic carbon.
While the C/N/P composition of dissolved and particulate matter is tied to Redfield stoichiometry,
the iron, silicon and carbonate contents of the particles are computed prognostically. Next to the
three organic detrital pools, carbonate and biogenic siliceous particles are modeled. Besides, the
model simulates dissolved inorganic carbon and total alkalinity. In PISCES, phosphate and nitrate +
ammonium are linked by constant Redfield ratio (C/N/P = 122/16/1). However, nitrogen fixation,
denitrification as well as external sources can modify this ratio. The distinction of two phytoplankton
size classes, along with the description of multiple nutrient co-limitations allows the model to
represent ocean productivity and biogeochemical cycles across major biogeographic ocean provinces
(Longhurst, 1998). PISCES has been successfully used in a variety of biogeochemical studies (e.g.
Bopp et al. 2005; Gehlen et al. 2006; 2007; Schneider et al. 2008; Steinacher et al. 2010; Tagliabue et
al. 2010, Séférian et al, 2013).
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II.3 Coupling and configuration
PISCES is coupled to NEMO-OPA via the TOP component that manages the advection/diffusion
equations of passive tracers but also the sources and sinks terms due to biogeochemistry. For this
regional configuration, physics and biogeochemistry are running simultaneously (“on-line” coupling),
at the same resolution.
The numerical scheme of PISCES for biogeochemical processes is forward in time (Euler), which does
not correspond to the classical leap-frog scheme used for the physical component. In order to
respect the conservation of the tracers, the coupling between biogeochemical and physical
components is done one time step over two. The time step for the biogeochemical model is twice
that of the physical component, i.e. 900 s.
The advection scheme TOP-PISCES is the same QUICKEST-Zalezak scheme used for the physical part.
The simulation started on January 30, 2002 and was initially launched up to December 23, 2011. For
the extension, the simulation restarted on August 31, 2011 and was extended up to December 27,
2014. Some modifications between the initial reanalysis and the extension for the physical
component are described in the associated Product User Manual Document CMEMS-IBI-PUM-005002-v2.0 (Sotillo et al, 2016). There is no change for the biogeochemical component.
The biogeochemical model is initialized with a previous IBI simulation at the same resolution. This
previous simulation was initialized by World Ocean Atlas 2001 for nitrate, phosphate, oxygen and
silicate (Conkright et al. 2002), with GLODAP climatology including anthropogenic CO2 for Dissolved
Inorganic Carbon and Alkalinity (Key et al. 2004) and, in the absence of corresponding data products,
with model climatological fields for the others variables. These same climatologies are also used to
force the open boundaries.
Boundary fluxes account for nutrient supply from three different sources: atmospheric deposition,
rivers for nutrients, dissolved inorganic carbon and alkalinity (Ludwig et al., 1996) and inputs of Fe
from marine sediments. For more details on external supply of nutrients, please refer to the
supplementary material of Aumont and Bopp (2006).
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III VALIDATION FRAMEWORK
We assess the system performance and the associated product quality by comparing biogeochemical
modeled fields (sections and maps) with data or climatologies (CLASS1) when they are available.
These visual comparisons are done using the time average of the biogeochemical fields over the
years 2003 to 2014. In the case of the monthly statistics presented in this document for chlorophyll,
euphotic depth and net primary productivity, the analysis commence in year 2002, either from the
month that the model simulations commenced (i.e. February 2002) or the observational data
became available, whichever the later.
The validation methodology and metrics classification is described in Lellouche et al. (2012). Table 3
summarizes the type of metrics used to monitor the system.
Table 3: List of metrics used to assess the system.
Variable
Region
MERSEA/GODAE
classification
Chlorophyll a
IBI
CLASS1
Chlorophyll a
IBI
CLASS3
Reference observational dataset
ESA CCI (2002 – 2013)
OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065
ESA CCI (2002 – 2013)
OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065
Globcolour (ACRI) (2014)
1 km AVW
Chlorophyll a
IBI
CLASS2
ESA CCI (2002 – 2013)
OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065
Globcolour (ACRI) (2014)
1 km AVW
Nitrates
IBI
CLASS1
WOA 2013 V2, NOAA (Garcia et al., 2014)
Phosphates
IBI
CLASS1
WOA 2013 V2, NOAA (Garcia et al., 2014)
Silicates
IBI
CLASS1
WOA 2013 V2, NOAA (Garcia et al., 2014)
Oxygen
IBI
CLASS1
WOA 2013 V2, NOAA (Garcia et al., 2014)
Ammonium
IBI
CLASS1
Iron
IBI
CLASS1
Phytoplankton
biomass in carbon
IBI
CLASS1
Primary production
IBI
CLASS1
VGPM, Oregon State University
Primary production
IBI
CLASS2
VGPM, Oregon State University
Primary production
IBI
CLASS3
VGPM, Oregon State University
Euphotic layer
IBI
CLASS1
Globcolour (ACRI) 1km AVW
Euphotic layer
IBI
CLASS2
Globcolour (ACRI) 1km AVW
Euphotic layer
IBI
CLASS3
Globcolour (ACRI) 1km AVW
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Amongst many statistics presented in this report is the Percentage of Bias (PB) (see Table 2 caption
for the equation), which has been previously cited as the performance indicator (Maréchal et al.,
2004). The model performance classification based on PB is explained in Table 4 below. Tables
presenting PB in this document have been colour-coded accordingly.
Table 4. Model performance scale
based on the Percentage of Bias.
PB
< 10
10 – 20
20 – 40
> 40
Performance
Excellent
Very good
Good
Poor / bad
Taylor diagrams (Taylor, 2001) are also included in this report and these present the statistics
computed for monthly and annual climatologies (i.e. in the case of simulated vs. observed
chlorophyll, primary productivity and euphotic depth: 2003 – 2014 averages, in the case of simulated
nitrates, phosphates, silicates and oxygen vs. WOA 2013: monthly and annual climatologies). ‘IBIRYS’
on Taylor diagram refers to the annual averages and all monthly climatologies are labelled
accordingly. It should also be noted that Taylor diagrams present Centered RMSD, which differs from
RMSD presented in Table 2 (equation given in Table 2 caption) and RMSD presented on the graphs
throughout the document. The equation for Centered RMSD included on Taylor diagram is presented
below:
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IV VALIDATION RESULTS
Available data to assess the quality of biogeochemical models is still scarce. This biogeochemical
system is evaluated by systematically comparing model fields to climatologies or monthly satellitederived data. Climatology maps are presented for a visual comparison and tables and graphs with
various statistics for monthly and annual means of chlorophyll, net primary productivity and euphotic
depth are also included.
This validation task is based on a 13-year hindcast simulation between 2002 and 2014. In this
document, climatologies are calculated for 2003-2014, whereas the monthly statistics are computed
for all months since 2002 when the model was initiated and/or satellite data is available.
IV.1 Chlorophyll
Figure 2 shows a comparison of averaged surface chlorophyll over the period 2003-2013 between
the IBI regional simulation (IBIRYS) and ESA-CCI product data. IBIRYS shows a good agreement with
satellite data. Highest concentrations situate along the coasts and most especially in the North Sea,
Skagerrak Strait and Kattegat Bay, and lowest concentrations are found in the subtropical gyre to the
south of the domain. However, open boundaries in the vicinity of the subtropical gyre are not well
reproduced due to the lack of realistic biogeochemical information to force conveniently the regional
configuration. In the Irish Sea, the concentrations are too weak while the Rhône river plume is
overestimated.
Figure 2: Concentration of chl-a: mean over the years 2003-2013 at sea surface (mg Chl.m-3) ; (left)
IBIRYS and (right) Chl-a data from ESA CCI L3 (OceanColour).
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The temporal variability is also assessed by comparing median and 80th percentile in model and
ocean color data (Figure 3). The model is able to reproduce the main features of the seasonal cycle: a
bloom in spring when the mixed layer, rich in nutrients, shoals and becomes shallower than the
euphotic layer; a decrease of chlorophyll concentration in summer due to a thin mixed layer very
poor in nutrients (nutrient limitation); a second bloom in autumn when the mixed layer is becoming
deeper and nutrients are entrained at its base; and in winter, a period of weak production due to
light limitation.
Figure 3 and Figure 6 also reveal that the amplitude of the spring bloom, as expressed by chlorophyll,
is too strong in the model although the phasing of the bloom is good. The duration of the bloom is
too short, though as chlorophyll concentration drops too quickly and remains below the observed
values during summer and autumn. The amplitude of the secondary bloom in autumn is also too low
in the model.
Figure 4 reveals that there is not a decreasing or increasing temporal trend in the median and 80th
percentile values of chlorophyll neither in the model nor in the observations. It is interesting to note,
that the modelled annual means of medians and 80th percentiles are higher in the model than in the
observations, which is contrary to the simple mean presented in Table 1. It leads to the conclusion
that the model tends to overestimate high concentrations of chlorophyll.
The analysis of monthly PB values (Table 6 and Figure 5) reveal that the model scores highest
(according to PB) generally between December – March and also in the month of June. The
correlation coefficient also shows a cyclical pattern, however, most of the peaks occur in autumn (i.e.
September, October) and most troughs in spring (i.e. April, May) (Figure 5 and Figure 7). RMSD
achieves highest value during the spring bloom and lowest in December, which can be expected
(Figure 6). The Taylor diagram (Figure 7) also points to a larger spread in chlorophyll concentrations
in the model between May – August and lower between September – April when compared to the
observations.
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Table 5: Annual statistics for modelled and observed chl-a (black – observations, red – model).
Mean
(mg m-3)
Std. deviation
(mg m-3)
RMS difference
(mg m-3 )
Mean difference
(mg m-3)
Percent Bias
(%)
Correlation
2003
0.6181
0.4946
0.8
0.5449
2004
0.6011
0.5427
0.7638
0.6034
2005
0.5875
0.5719
0.7383
0.5879
2006
0.5813
0.5618
0.7605
0.5966
2007
0.6238
0.5443
0.8315
0.5773
2008
0.6125
0.5391
0.8501
0.5715
2009
0.6313
0.5976
0.7442
0.5819
2010
0.5788
0.5306
0.7778
0.5863
2011
0.6127
0.5467
0.7485
0.5923
2012
0.6043
0.6622
0.6849
0.7906
2013
0.5803
0.6351
0.745
0.7809
2014
0.6605
0.6056
1.1837
0.7359
0.5606
0.517
0.4953
0.5293
0.5494
0.5599
0.4829
0.4967
0.5038
0.4867
0.4549
0.8275
0.1234
0.0584
0.0156
0.0195
0.0795
0.0734
0.0337
0.0482
0.0661
-0.0579
-0.0549
0.0549
19.97
0.73
9.72
0.74
2.66
0.74
3.36
0.72
12.75
0.76
11.99
0.76
5.34
0.76
8.32
0.77
10.78
0.75
9.57
0.79
9.46
0.83
2009
13.83
4.5
30.06
44.83
36.45
15.54
20.96
23.55
30.19
38.32
35.6
17.54
2010
12.71
0.06
13.13
36.3
30.78
14.29
14.92
28.3
34.44
39.17
32.08
3.97
2011
8.77
2.24
36
7.56
7.27
6.1
12.13
36.96
33.3
31.65
30.17
9.23
8.31
0.72
Table 6: Monthly Percentage of Bias for chlorophyll.
2002
January
February
March
April
May
June
July
August
September
October
November
December
8.86
3.31
34.45
26.83
16.8
1.33
18.63
32.04
39.44
31.75
5.19
2003
20.44
14.5
7.38
18.39
19.19
19.01
41.78
43.03
46.74
45.66
43.34
6.03
2004
13.93
8.54
8.31
23.72
50.24
0.51
26.09
39.83
38.07
31.37
27.05
8.66
2005
2
1.62
45.25
33.41
24.4
3.29
22.54
25.92
25.43
29.63
30.77
7.29
2006
3.26
2.71
33.26
47.02
34.71
31.27
17.43
27.49
35.2
35.54
47.24
20.46
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2007
24.59
9.89
9.21
42
6.04
5.45
19.91
25.39
40.18
35.73
41.15
17.81
2008
12.68
4.11
7.25
23.6
19.75
4.69
14.99
30.51
30.07
33.81
28.61
3.41
2012
0.17
16.02
47.79
49.84
92.8
13.41
8.95
27.61
22.3
7.8
5.46
23.18
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2013
24.42
21.53
54.12
43.19
56.73
35.52
12.96
12.61
17.8
22.72
14.29
24.31
2014
27.02
1.26
38.61
26.42
18.78
2.97
16.24
21.89
31.6
32.39
31.83
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Figure 3: Time series of the median and the 80th percentile Chl-a at sea surface over the years 20022014 from IBIRYS, ESA-CCI (2002-2013) and Globcolour (2014).
Figure 4: Trend comparison: Annual means of monthly medians and monthly 80th percentiles
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Figure 5: Time series of correlation coefficient and percentage of bias for chl-a.
Figure 6: Time series of mean chl-a concentration in IBIRYS, ESA-CCI and Globcolour and RMSD for
chl-a.
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Figure 7: Taylor diagram for annual (solid circle) and monthly (crosses) chl-a 2003-2013
climatologies.
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IV.2 Nitrates
Figure 8 shows a comparison at sea surface of the nitrate concentration derived from climatological
data from the World Ocean Atlas 2013 and predicted by the model. Globally, there is a good
agreement between them in the northern part of the domain, whereas the subtropical gyre and the
Mediterranean Sea exhibit too low surface concentrations compared to WOA2013. The bias is also
manifested in Figure 12 and it develops in May and persists up to October. Given that nitrates are
underpredicted in the southern parts of the domain, this bias can thus be attributed to the
overprediction in the northern parts of the domain. This bias and the overall negative mean error
(see Table 2) agree well with the overall underprediction of chlorophyll in the domain, as expressed
by chlorophyll mean (Table 1) and positive mean error (Table 2). It is worth noting that there is an
elevated spring phytoplankton bloom in the model (see section IV.1) followed by its too quick
degradation and underprediction of chlorophyll in the summer . It is thus likely that part of the
excessive nitrates in the model in the summer months are regenerated nitrates derived from
deceased phytoplankton. Poorer model performance also manifests in the values of other statistics,
namely the correlation coefficient, PB (both on Figure 13), standard deviation and Centred RMSD
(both on Figure 14), whereby all of these deteriorate markedly for the time period May-October.
Better model performance during winter is particularly apparent on the Taylor diagram in Figure 14,
where all three presented statistics improve significantly for the time period November – April.
The vertical profiles (Figure 11) from the model and WOA2013 are in good agreement with some
more pronounced mismatch around the water depths 700 – 1000m. The more pronounced bias
around these water depths can also be seen in Figure 10 and is most likely attributed to the model
not reproducing the shallowing and deepening of mixed layer depth with a high degree of accuracy.
One can expect an elevated bias around the MLD in the ocean models though. Vertical transects
presented in Figure 9Figure 9 reveal good agreement between the model and WOA2013
climatologies. The aforementioned mixed layer depth is pronounced on these transects and is
located below 500m depth and extending down to almost 1000m on some of the transects. In
general the mixed layer depth, as expressed in lower nitrate concentrations appears to be deepening
towards the coasts (see transects 40N and 50N) and when moving from the south to the north of the
domain (see transect 18W). There is a good agreement between the model and WOA2013 in this
regard.
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Figure 8: Concentrations of nitrate. Mean over the years 2003-2014 at sea surface (µmolL-1)
(left) IBIRYS ; (right) Climatology WOA 2013.
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18W
18W
30N
30N
40N
40N
50N
50N
Figure 9: Mean of nitrate concentration in sea water along various transects in µmolL-1. Left panel:
IBIRYS; right panel: WOA 2013.
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40N
50N
Figure 10: Bias (WOA 2013 – IBIRYS) in mean nitrate concentration in sea water along various
transects in µmolL-1.
Figure 11: Vertical profile of mean nitrate concentration over the IBI area in µmolL-1 from IBIRYS
and WOA 2013.
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Figure 12: Time series of mean monthly nitrate concentrations and RMSD for IBIRYS (average 2003
– 2014) and WOA 2013 climatology at sea surface.
Figure 13: Time series of correlation coefficient and percentage of bias for nitrates for IBIRYS
(average 2003 – 2014) and WOA 2013 climatology at sea surface.
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Figure 14: Taylor diagram for annual and monthly nitrate for IBIRYS (2003 – 2014 average) and
WOA 2013 climatologies.
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IV.3 Phosphate
Similar conclusions to nitrates (section IV.2) can be drawn for phosphates. Figure 15 shows a
comparison at sea surface of the phosphate concentration derived from climatological data from the
World Ocean Atlas 2013 and predicted by the model. Globally, there is a good agreement between
them in the northern part of the domain, whereas the subtropical gyre and the Mediterranean Sea
exhibit too low surface concentrations compared to WOA2013. Contrary to nitrates though, the
model has a negative bias compared to WOA2013 when the entire IBI Service domain is taken into
account, both at surface (Figure 19) and throughout the water depth (Figure 18), where the modelpredicted phosphates are almost always below the observed. As opposed to nitrates then, the mean
error presented in Table 2 is positive, however, in the percentage terms it is of comparable
magnitude (just above 10%) to that for nitrates.
Similarly to nitrates, poorer model performance in summer (May – September) is clearly manifested
on the Taylor diagram (Figure 21), although not so clearly evidenced on the remaining figures. For
example, the plot of mean phosphates presented in Figure 19 suggests the worst model performance
in the autumn. It is interesting to note further that the monthly RMSD, in turn (also in Figure 19),
does not suggest a deterioration of the model performance during any particular season. The
correlation coefficient, however, drops markedly during the summer months and early autumn
(Figure 20), whereas, the percentage of bias presented in the same Figure achieves highest values
(worst model performance) in the autumn and confirms the pattern observed on the plot showing
mean monthly phosphates presented in Figure 19.The vertical profiles (Figure 18) from the model
and WOA2013 are generally in good agreement, although a consistent underprediction by the model
over all depths can be seen. The underprediction of surface phosphates in subtropical gyre is clearly
manifested in Figure 17 (i.e. section 30N). Vertical transects presented in Figure 16 lead to the similar
conclusions to those presented for nitrates in section IV.2.
Figure 15: Concentrations of phosphate. Mean over the years 2003-2014 at sea surface (µmolL-1)
(left) IBIRYS ; (right) Climatology WOA 2013.
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18W
30N
30N
40N
40N
50N
50N
Figure 16: Mean of phosphate concentration in sea water along various transects in µmolL-1. Left
panel: IBIRYS; right panel: WOA 2013.
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50N
Figure 17: Bias (WOA 2013 – IBIRYS) in mean phosphate concentration in sea water along various
transects in µmolL-1.
Figure 18: Vertical profile of mean phosphate concentration over the IBI area in µmolL-1 from
IBIRYS and WOA 2013.
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Figure 19: Time series of mean monthly phosphate concentrations and RMSD for IBIRYS (average
2003 – 2014) and WOA 2013 climatology at sea surface.
Figure 20: Time series of correlation coefficient and percentage of bias for phosphates for IBIRYS
(average 2003 – 2014) and WOA 2013 climatology at sea surface.
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Figure 21: Taylor diagram for annual and monthly phosphates for IBIRYS (2003 – 2014 average)
and WOA 2013 climatologies.
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IV.4 Silicate
Figure 22 shows a comparison at sea surface of the silicate concentration derived from climatological
data from the World Ocean Atlas 2013 and predicted by the model. The undeprediction in the
subtropical gyre observed for nitrates and phosphates extends to further areas in the case of
silicates, namely to the Atlantic Iberian coast, the Bay of Biscay, the Celtic Sea and the North Sea.
There is a good agreement for the remaining northern part of the domain and in the areas to the
west and south-west of Ireland. Although the comparison of model’s and WOA’s means and RMSD
presented in Figure 26 and PB presented in Figure 27 would suggest that the model performs better
over summer than winter, the Taylor diagram presented in Figure 28 suggests that, similarly to
nitrates and phosphates, the model performs worse between May and September, although overall
all the statistics are markedly poorer for all the months when compared to nitrates and phosphates.
As opposed to nitrates and phosphates, the dynamics of silicates in the model is driven by diatoms,
therefore one can expect that there will be less similarities in the model skill compared to the
previous two nutrients.
Globally, the vertical profiles presented in Figure 25 compare well, however, some more pronounced
biases can be observed at individual transects. For example, Figure 24 reveals that the model tends
to overpredict silicates throughout the entire water columns in the south of the domain and
underpredict in the north of the domain (see the shift from negative to positive bias when moving
from south to north along transect 18W and also compare transects 30N and 50N). It also appears
from Figure 25 that contrary to nitrates and phosphates the boundary layer between silicate-rich and
silicate-poor(er) waters is not so pronounced and their concentrations increase gradually with depth
with some more pronounced boundary around the 3500m water depth. This deep boundary is
further confirmed in , and despite the biases noted in Figure 24, transects presented in this Figure
compare visually very well with WOA2013.
Figure 22: Concentrations of silicate. Mean over the years 2003-2014 at sea surface (µmolL-1) (left)
IBIRYS ; (right) Climatology WOA 2013
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18W
30N
30N
40N
40N
50N
50N
Figure 23: Mean of silicate concentration in sea water along various transects in µmolL-1. Left
panel: IBIRYS; right panel: WOA 2013.
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Figure 24: Bias (WOA 2013 – IBIRYS) in mean silicate concentration in sea water along various
transects in µmolL-1.
Figure 25: Vertical profile of mean silicate concentration over the IBI area in µmolL-1 from IBIRYS
and WOA 2013.
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Figure 26: Time series of mean monthly silicate concentrations and RMSD for IBIRYS (average 2003
– 2014) and WOA 2013 climatology at sea surface.
Figure 27: Time series of correlation coefficient and percentage of bias for silicates for IBIRYS
(average 2003 – 2014) and WOA 2013 climatology at sea surface.
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Figure 28: Taylor diagram for annual and monthly silicates for IBIRYS (2003 – 2014 average) and
WOA 2013 climatologies.
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IV.5 Oxygen
Figure 29 reveals that the model-predicted average oxygen at sea surface is in very good agreement
with the WOA 2013 climatology. This is to be expected, as the sea surface temperature is assimilated
in the physical model and the temperature constraints strongly the solubility of atmospheric oxygen
at sea surface. The annual pattern, expressed as mean O2, is well represented (Figure 33) with almost
a perfect match between May-September and slight overprediction by the model between OctoberApril. All the computed statistics point to a very good model performance at sea surface. Based on
the values of PB (Figure 34) the model is actually classified as excellent throughout all months. The
correlation coefficient presented in the same Figure 34 is also mainly above 0.95. An excellent model
performance is further confirmed in the Taylor diagram (Figure 35), presenting high correlation
coefficients (only August is outstanding with the value just below 0.9), normalized standard
deviations of around 1 and low centered RMSD in the region of 0.3. Individual transects also point to
a good agreement between the model and the climatology over the depth (see Figure 30). Globally,
the vertical profile is well represented by the model (Figure 32), including the oxygen minimum (~193
µmol L-1) at around 900 m water depth. The core is too much pronounced but, oxygen concentration
above and below this oxygen minimum area is somewhat overestimated in the model (Figures 31
and 32).
The overestimation of surface oxygen has previously been confirmed in Table 1 and Table 2,
however, it should be stressed that overall, based on the statistics provided in this report the model
score is excellent.
Figure 29: Concentrations of dissolved oxygen. Mean over the years 2003-2014 at sea surface
(µmolL-1); (left) IBIRYS; (right) Climatology WOA 2013.
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18W
30N
30N
40N
40N
50N
50N
Figure 30: Mean of oxygen concentration in sea water along various transects in µmolL-1. Left
panel: IBIRYS; right panel: WOA 2013.
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Figure 31: Bias (WOA 2013 – IBIRYS) in mean oxygen concentration in sea water along various
transects in µmolL-1.
Figure 32: Vertical profile of mean oxygen concentration over the IBI area in µmolL-1 from IBIRYS
and WOA 2013.
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Figure 33: Time series of mean monthly oxygen concentrations and RMSD for IBIRYS (average 2003
– 2014) and WOA 2013 climatology at sea surface.
Figure 34: Time series of correlation coefficient and percentage of bias for oxygen for IBIRYS
(average 2003 – 2014) and WOA 2013 climatology at sea surface.
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Figure 35: Taylor diagram for annual and monthly oxygen at sea surface for IBIRYS (2003 – 2014
average) and WOA 2013 climatologies.
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IV.6 Ammonium
Ammonium is present in the marine environment in much lower concentrations than nitrates and is
mainly confined to the coastal areas near river mouths and estuaries. This is confirmed on the map in
Figure 36, where the highest ammonium concentration is found near the River Rhone mouth, along
the French Atlantic coast, in the Irish Sea, the English Channel and in the North Sea. There are very
low concentrations in the subtropical gyre and in the Mediterranean Sea, and the concentration
increases gradually when moving towards the northern parts of the model domain. As regards the
maximum concentrations outside of the coastal regions and the shelf seas, these reach 0.2-0.3
µmolL-1 in the north-west of the model domain.
Vertical transects presented in Figure 37 reveal that the maximum ammonium concentration in the
model is found below the surface. The depth of this maximum varies from around 100 m in the south
of the domain to less than 50 m in the north. Ammonium is not present in deep waters. This
maximum is most likely the result of remineralization of nitrogen from decaying phytoplankton that
settles through the water column. When averaged over the entire model domain, ammonium is
found at its highest concentration at the surface (c. 0.27 µmolL-1), then drops slightly and increases
again to reach the second peak of the same concentration at c. 40 m depth, and subsequently falls
quickly to reach very low levels below 200 m water depth (see Figure 38).
Figure 36: Mean 2003-2014 ammonium concentration in IBIRYS at sea surface (µmolL-1).
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40N
50N
Figure 37: Mean of ammonium concentration in IBIRYS along various transects in µmolL-1. Upper
1000 m is shown.
Figure 38: Vertical profile of mean ammonium concentration over the IBI area in µmolL-1 from
IBIRYS. Upper 1000 m is shown.
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IV.7 Iron
Main sources of iron in the ocean are considered to be atmospheric dust, hydrothermal vents and
oceanic sediments. Iron distribution in the IBIRYS model is as follows (see Figure 39): highest
concentrations are found to the south of the Canary Islands, along the Mediterranean coasts of Spain
and France, and in the shelf waters along the Atlantic coast of France, the Irish Sea, the English
Channel and the North Sea. The lowest concentration is found in the open ocean to the west of the
Iberian Peninsula.
Vertical transects presented in Figure 40 reveal that the minimum iron concentration in the water
column is usually found in the surface water of open ocean, except for transect 30N (just north of the
Canary Islands) where the minimum is found between 100-200 m water depth; high surface
concentrations come from Saharan dust. Strong gradient of concentrations when moving from the
open ocean to shelf waters observed in Figure 39 is confirmed in the figure showing transect 50N. In
the deep waters modelled iron concentrations oscillate around 1 µmolL-1. When averaged over the
entire model domain, however, iron is found at its highest concentration at the surface (c. 1.25
µmolL-1), then drops significantly to reach the minimum of just over 0.7 µmolL-1 at around 170 m
water depth, and increases again to reach the concentration of just above 1 µmolL-1 at c. 800 m (see
Figure 41).
Figure 39: Mean 2003-2014 iron concentration in IBIRYS at sea surface (µmolL-1).
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30N
40N
50N
Figure 40: Mean of iron concentration in IBIRYS along various transects in µmolL-1. Upper 1000 m is
shown.
Figure 41: Vertical profile of mean iron concentration over the IBI area in µmolL-1 from IBIRYS.
Upper 1000 m is shown.
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IV.8 Phytoplankton biomass in carbon
Spatial distribution of phytoplankton carbon presented in Figure 42 follows closely that of
chlorophyll presented in section IV.1 and is not discussed here in further detail.
However, Figure 43, provides further insight into a vertical distribution of phytoplankton carbon in
the water column. As can be seen, in the south of the domain (see transects 18W, 30N and the
Mediterranean part of transect 40N), it reaches maximum at subsurface waters (as deep as 100 m),
whereas in the north of the domain the maximum can be found at surface.
Figure 42: Mean 2003 – 2014 concentration of phytoplankton biomass in carbon in IBIRYS at sea
surface in µmolC L-1.
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50N
Figure 43: Mean 2003 – 2014 phytoplankton carbon concentration in IBIRYS along various transects
in µmolL -1. Upper 1000 m is shown.
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IV.9 Primary production
As evidenced in Figure 44, the depth integrated net primary productivity is significantly different
when compared to the VGPM model. Based on the domain-wide mean values presented in Table 7,
the model underpredicts the rate by a factor of 2.5 to 3 compared to VGPM. Based on the
Percentage of Bias the model skill is assessed as poor / bad (Table 7 and Table 8). Significant
underestimation of modelled primary productivity along the European Atlantic coasts has previously
been reported for other models (e.g. Schourup-Kristensen et al., 2012). As well Campbell et al.
(2002), comparing primary production models (such as VGPM algorithm) and in-situ measurements
of NPP (C-14 incubation), reported that the “best performing algorithm agree with in-situ estimates
within a factor 2”. So direct comparison is maybe not straightforward. Instead, normalized NPP
appears a good approach. Figure 45 shows that spatial distributions of the magnitudes of primary
productivity in the model and VGPM are very similar. Further evidence, e.g. the intra-annual patterns
presented in Figure 48 and Figure 51, suggests that the phasing is generally in good agreement,
although the model tends to predict the peak productivity ~1 month ahead of VGPM in most years.
The correlation coefficient, PB (both on Figure 50) and RMSD presented in Figure 51 exhibit a cyclical
pattern. The lowest PB tends to occur in March-April, whilst the highest in September-October,
however, all values are in the poor / bad model score (due to the shift in mean value). The highest
correlation tends to occur in late summer / September. As regards RMSD, its peaks and troughs
appear to coincide with peaks and troughs in the VGPM data. This cyclical pattern in the computed
statistics is further confirmed on the Taylor diagram presented in Figure 52, with, the correlation
coefficient, for example having visibly higher values for the months June-October when compared to
November-May.
Similarly to chlorophyll, there is a lack of visible inter-annual trend as evidenced in Figure 49. Vertical
transects presented in Figure 46 confirm that the productivity is not taking place below the expected
euphotic depth. Also, as expected in the oligotrophic regions in the south of the domain the primary
productivity extends deeper in the water column (down to c. 150 m), whereas it is shallowing
towards the north of the domain (less than 100 m along transect 50N). When averaged over the
entire domain, the rate of primary productivity decreases logarithmically over depth from c.20 gC m-3
day-1 to very low levels below c. 120 m depth.
The obtained validation results, i.e. overestimated spring blooms expressed as chlorophyll (see
section IV.1) and underestimated net primary productivity, point to the possibility that the
biogeochemical cycles in the present model are not sufficiently controlled by the processes such as
zooplankton grazing, phytoplankton die-off, settling rates or remineralisation rates, which will have
to be reviewed in an attempt to improve the model’s ability to reproduce a more accurate seasonal
pattern in chlorophyll-a, net primary production, nutrients, ….
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Figure 44: Maps of depth-integrated primary productivity. Mean over the years 2003-2014 in
mgC m-2 day-1; (left) IBIRYS; (right) VGPM.
Figure 45: Maps of normalized depth-integrated primary productivity. Mean over the years 20032014; (left) IBIRYS; (right) VGPM. Normalization was performed by dividing the IBIRYS and VGPM
depth-integrated primary productivities by their respective means (see Table 1).
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18W
30N
40N
50N
Figure 46: Mean 2003 – 2014 primary productivity in IBIRYS along various transects in gC m-3 day-1.
Upper 1000 m is shown.
Figure 47: Vertical profile of mean primary productivity over the IBI area in gC m-3 day-1 from
IBIRYS. Upper 200 m is shown.
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Figure 48: Time series of the median and the 80th percentile primary productivity over the years
2002-2014 from IBIRYS and VGPM model.
Figure 49: Trend comparison: Annual means of monthly medians and monthly 80th percentiles of
primary productivity.
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Figure 50: Time series of correlation coefficient and percentage of bias for primary productivity.
Figure 51: Time series of mean primary productivity for IBIRYS and VGPM and time series of RMSD .
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Table 7: Annual statistics for modelled and observed primary productivity (black – observations, red – model).
Mean
(mg m-2 d-1)
Std. deviation
(mg m-2 d-1)
RMS difference
(mg m-2 d-1)
Mean difference
(mg m-2 d-1)
Percent Bias (%)
Correlation
2003
899.1
296.2
600.3
115.1
2004
883.9
306.9
576.3
116.5
2005
853.5
323.5
528.9
106.5
2006
828.2
318
542.2
107.4
2007
871.7
305.9
594.6
115.1
2008
857.9
301.6
585.8
117.7
2009
926.4
323
573.1
110.2
2010
845.2
289.8
555.2
122.5
2011
914.9
298.2
549.8
118.4
2012
896.3
332.7
494.2
134.1
2013
842.9
324.7
505.9
147.7
2014
900
316
583.6
142.8
802.61
769.82
703.3
695.69
764.71
752.63
782.43
728.2
775.51
690.08
650.34
750.74
602.9
577
530
510.2
565.8
556.2
603.5
555.3
616.7
563.6
518.2
583.9
67.06
0.67
65.28
0.64
62.1
0.69
61.6
0.7
64.9
0.75
64.84
0.72
65.14
0.73
65.71
0.75
67.41
0.73
62.88
0.78
61.48
0.83
64.89
0.83
Table 8: Monthly Percentage of Bias for primary productivity.
2002
January
February
March
April
May
June
July
August
September
October
November
December
62.47
64.34
68.56
72.13
72.96
66.2
2003
58.89
55.86
51.03
52.28
58.86
69.58
70.48
70.24
72.59
73.78
73.91
64.21
2004
58.92
54.9
51.59
50.58
53.4
63.15
70.64
71.14
72.34
70.37
70.57
62.61
2005
54.29
44.65
42.07
51.5
56.04
62.72
65.16
65.69
67.5
69.5
70.98
62.72
2006
51.64
44.15
40.26
48.2
53.33
56.6
64.14
66.36
71.08
72.01
75.48
68.9
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2007
62.32
57.04
47.28
50.43
62.07
65.24
66.49
67.69
70.8
71.34
70.59
64.77
2008
61.84
54.91
53.2
48.31
61.87
64.25
66.24
68.71
70.1
71.24
70.12
58.25
2009
56.28
47.6
51.88
47.99
56.02
68.53
68.25
69.39
70.63
73.24
75.19
70.65
2010
63.57
57.76
52.12
53.47
57.37
65.53
65.84
67.82
72.02
74.56
72.11
67.29
2011
63.3
57.93
50.31
64.71
65.07
63.8
67.84
70.89
70.5
71.46
73.11
66.98
2012
57.11
43.32
45.14
48.68
48.4
66.44
68.32
70.97
70.21
68.8
71.16
66.32
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2013
61.62
47.54
39.63
47.24
49.31
58.42
67.37
67.15
69.6
70.69
71.26
65.68
2014
62.47
57.35
47.55
54.64
56.91
64.77
67.16
67.73
69.69
72.38
74.61
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Figure 52: Taylor diagram for annual and monthly primary productivity for IBIRYS (2003 – 2014
average) and VGPM climatologies.
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IV.10 Euphotic layer depth
As evidenced in Figure 53, the euphotic layer depth is mainly well represented in the model. Some
discrepancies exist in the north-west part of the domain, where the model overestimates its depth
and in the Mediterranean Sea, where it is underpredicted. The deepest euphotic depth is predicted
and observed in the oligotrophic regions of the subtropical gyre and the Mediterranean Sea, and the
shallowest in the productive regions, such as the North Sea. Domain-wide, the euphotic layer depth
reaches the minimum in spring, coinciding with the phytoplankton bloom, and the maximum in
winter (Figure 55). The timing of peaks and troughs between the model and the observations
matches very well. As regards the amplitudes, the maximum depth (that occurs in winter) tends to
be over-predicted by the model, whereas the minima agree well. It is interesting to note a cyclical
nature of the correlation coefficient (Figure 54), which reaches low values in winter and maximum
values in summer and early autumn. This is confirmed by the values of PB, which fall into an
“excellent” category in July and August for all years and for September of most years, whereas its
October-December values downgrade to “very good” and occasionally to “good” (see Table 10). The
Taylor diagram (Figure 56), in turn, suggests that the December-February months are characterised
by the poorest skill, whereas the remaining months are comparable.
On the annual basis, the PB classifies the model as excellent for years 2004-2014, only year 2003 is
ranked lower as very good (see Table 9). It is worth noting in the same table that some years are
characterized by markedly higher statistics. For example, in years 2006 and 2014 the domainaveraged euphotic depth is less than 1 m greater in the model than in the observations. Overall, the
euphotic depth is overestimated by the model around 3m during the length of the simulation.
Figure 53: Euphotic layer depth (m). Mean over the years 2003-2014; (left) IBIRYS; (right)
Globcolour.
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Figure 54: Time series of correlation coefficient and percentage of bias for euphotic layer depth.
Figure 55: Time series of mean euphotic layer depth for IBIRYS and Globcolour and time series of
RMSD.
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Figure 56: Taylor diagram for annual and monthly euphotic layer depths for IBIRYS (2003 – 2014
average) and Globcolour climatologies.
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Table 9: Annual statistics for modelled and observed euphotic layer depth (black – observations, red – model).
Mean (m)
Std. deviation (m)
RMS difference (m)
Mean difference (m)
Percent Bias (%)
Correlation
2003
55.45
63.16
17.92
19.57
13.3592
-7.7119
13.91
0.83
2004
55.77
60.62
17.54
18.43
11.5591
-4.8501
8.7
0.83
2005
56.54
58.01
16.74
15.57
9.2777
-1.4643
2.59
0.84
2006
57.57
58.22
16.97
15.28
7.5508
-0.6487
1.13
0.9
2007
57.28
60.93
18.57
18.38
9.1795
-3.6556
6.38
0.9
2008
57.44
61.19
18.62
19.46
10.2871
-3.7515
6.53
0.87
2009
55.81
58.45
17.85
15.98
8.6541
-2.6399
4.73
0.89
2010
58.93
61.82
19.08
20.21
10.5154
-2.8946
4.91
0.87
2011
56.22
61.13
17.91
18.75
10.5424
-4.9106
8.73
0.87
2012
55.85
57.66
17.12
16.6
7.822
-1.8085
3.24
0.9
2013
58.11
59.33
17.45
18.73
7.448
-1.2213
2.1
0.92
2014
59.19
59.5
17.53
17.06
7.4034
-0.3101
0.52
0.91
2011
11
9.27
1.86
1.08
1.73
5.05
0.38
5.78
8.17
10.45
18.33
19.94
2012
9.4
1.52
9.99
15.9
17.7
5.81
1.19
2.73
5.92
8.23
13.43
12.38
2013
8.62
2.9
10.75
12.43
13.74
11.34
1.74
0.65
6.87
10.33
11.1
12.26
2014
5.02
3.81
13.11
17.99
19.8
13.42
9.22
4.82
2.67
11.48
17.15
23.7
Table 10: Monthly Percentage of Bias for euphotic layer depth.
2002
January
February
March
April
May
June
July
August
September
October
November
December
9.81
15.59
10.69
8.88
2003
9.64
14.09
9.61
0.76
0.5
7.2
7.68
7.64
14.09
20.21
23.43
22.46
2004
14.58
14.56
8.17
0.99
9.19
7.37
1.88
3.23
10.23
10.91
14.98
15.08
2005
4.91
1.03
7.41
11.2
11.4
7.19
4.03
2.53
1.72
6.89
11.53
14.67
2006
6.04
3.22
12.65
18.7
12.92
11.87
7.51
0.79
5.21
10.38
17.62
21.4
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2007
10.39
4.98
1.89
11.89
5.16
1.07
0.36
2.95
8.74
11.33
12.45
14.26
2008
12.16
6.14
0.64
8.17
4.72
3.83
2.45
2.54
8.4
11.79
15.86
12.88
2009
3.76
4.3
6.69
15.56
12.14
4.02
4.31
3.65
7.87
10.82
12.59
16.22
2010
9.18
6.47
1.54
7.76
9.96
5.11
3.83
0.12
3.91
12.86
13.5
13.13
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