QUALITY INFORMATION DOCUMENT North West

QUALITY INFORMATION DOCUMENT
North West European Shelf Reanalysis
NORTHWESTSHELF_REANALYSIS_PHYS_004_009 and
NORTHWESTSHELF_REANALYSIS_BIO_004_011
Issue: 3.0
Contributors: Sarah Wakelin, James While, Robert King, Enda O’Dea, Jason Holt, Rachel Furner, John Siddorn,
Matthew Martin, Robert McEwan ,Ed Blockley and Jonathan Tinker
Approval Date by Quality Assurance Review Group :
CHANGE RECORD
When the quality of the products changes, the QuID is updated and a row is added to this table. The
third column specifies which sections or sub-sections have been updated. The fourth column should
mention the version of the product to which the change applies.
Issue
Date
§
Description of Change
Author
Validated By
1.0
January
2014
All
Creation of the document Sarah Wakelin
for V4.0
Ed Blockley
2.0
December
2014
All
Addition
of
biological Robert McEwan
product
assessment
(BIO_004_011)
Ed Blockley
2.1
February
2015
All
Revision after acceptance
V5
Ed Blockley
2.2
13/03/2015
I.2, Warning remarks after
II
evidence of 004_011
interannual nutrient drifts
2.3
May 1 2015
all
Change format to fit CMEMS
graphical rules
3.0
21/01/2016
all
Update for V2 (time series
extension to 2014 and
addition of MLD)
Sarah Wakelin, Jon
Tinker
Marina Tonani
3.0
01/04/2016
all
Revision after V2 AR
Marina Tonani
Marina Tonani
Ed Blockley
L. Crosnier
TABLE OF CONTENTS
I
Executive summary ....................................................................................................................................... 4
I.1 Products covered by this document ........................................................................................................... 4
I.2 Summary of the results ............................................................................................................................... 4
I.3 Estimated Accuracy Numbers.................................................................................................................... 5
II
Production Subsystem description ................................................................................................................ 6
III
Validation framework ............................................................................................................................... 9
IV
Validation results ......................................................................................................................................... 11
IV.1 Temperature ........................................................................................................................................... 11
IV.2 Salinity ..................................................................................................................................................... 22
IV.3 Currents .................................................................................................................................................. 31
IV.4 Tidal analysis .......................................................................................................................................... 33
IV.5 Transport ................................................................................................................................................ 35
IV.6 Mixed layer depth................................................................................................................................... 40
VII.1 Chlorophyll............................................................................................................................................ 47
VII.2 Nutrients ................................................................................................................................................ 50
VIII
IX
Quality changes since previous version .................................................................................................. 52
References.................................................................................................................................................... 53
I
EXECUTIVE SUMMARY
I.1 Products covered by this document
NORTHWESTSHELF_REANALYSIS_PHYS_004_009 and NORTHWESTSHELF_REANALYSIS_BIO_004_011:
The physical and biogeochemical parts of a North West European Shelf Reanalysis performed at
~7km resolution.
For the physical product, comparisons are made to the North West European Shelf Hindcast at
~12km resolution POLCOMS_REANALYSIS.
I.2 Summary of the results
The quality of the NWS reanalysis simulation NORTHWESTSHELF_REANALYSIS_PHYS_004_009 from
01/01/1984 to 30/06/2014 has been assessed by comparison with observations. Results from the
corresponding control run and the hindcast product. POLCOMS_REANALYSIS covering 01/01/1964 to
31/12/2004, are included for validation purposes. The results for the NWS reanalysis are summarised
below.
Temperature: Integrated over the whole domain, the bias in surface temperature is 0.4°C. Mean
biases in the near surface layer (above 5m depth) have magnitude less than ~0.5°C while the largest
errors ~-2°C are in the Bay of Biscay between 800m and 2000m deep.
Salinity: Biases, on the practical salinity scale, are generally of magnitude less than 0.5. However, in
the coastal regions of the Southern Bight of the North Sea, salinity is typically ~2 too fresh while the
surface layer in the Irish Sea is ~0.5 too saline.
Currents: Comparison with climatology at 15m depth shows that the reanalysis simulation
reproduces major current systems in the region.
Transport: For the transport of volume through sections there is fair agreement with the (limited)
observational estimates available.
Tidal elevations: The M2 constituent of tidal elevation has RMSE of 11.6 cm for amplitude and 15.6°
for phase. The amplitude is underestimated in the Irish Sea and Southern Bight and overestimated in
large tidal areas such as the Bristol Channel. The phase error is largest in the Southern Bight and
German Bight.
The quality of the NWS biological product NORTHWESTSHELF_REANALYSIS_BIO_004_011 has been
assessed by comparison with observations during the period 03/01/1989 to 31/12/2003. The results
for the NWS biogeochemical reanalysis are summarised below.
Mixed layer depth: data for the assessment was generally sparse. Model errors were small during
the spring/summer months and larger during winter when data was sparser. The model tends to
underestimate the mixed layer depth in deep waters and overestimate it in shallower waters.
Chlorophyll: For the period analysed the model is shown to have a negative bias in surface
chlorophyll over the whole domain and all sub-regions with the mean error being -0.143. Compared
to in-situ observations the inter-annual variability is captured well and there appears to be no
appreciable drift in model bias.
Nutrients: Few in-situ observations were available for comparison so model output has been
qualitatively compared to World Ocean Atlas Climatology. Whilst summer depletion and winter
recharge of nitrate, phosphate and silicate are captured there appears to be a reduction in total
concentrations over the period assessed.
Important notice: Further evaluation of product NORTHWESTSHELF_REANALYSIS_BIO_004_011 in
March 2015 has revealed that there are substantial inter-annual drifts in the nutrient fields
throughout the reanalysis period. Because the reanalysis was run in four sections [January 1984 ->
March 1989; April 1989 -> December 2003; January 2004 -> June2014] and re-initialised at the start
of each period, this also implies that there are temporal discontinuities at the boundaries between
the four periods. These drifts and discontinuities are likely to have impacts on other parts of the
biogeochemical system, which may be important in a number of user applications.
Therefore users are strongly advised to contact the Service Desk for advice before embarking on
projects using the biogeochemistry product NORTHWESTSHELF_REANALYSIS_BIO_004_011. It is
intended that these issues will be resolved at the next release of this product.
I.3 Estimated Accuracy Numbers
Variable
Metrics
Units
Decimal places
temperature
mean bias, standard deviation, RMS
°C
2
salinity
mean bias, standard deviation
tidal elevation
harmonic constants
RMS
cm / degree
currents
mean
m s-1
transport
mean, standard deviation
Sv
2
heat flux
mean
W m-2
1
water flux
mean
kgm-2s-1
1
Mixed layer depth
mean
m
0
2
2/1
II
PRODUCTION SUBSYSTEM DESCRIPTION
Production centres name: Met Office, UK
Production subsystem name: North West European Shelf Reanalysis physics and biogeochemistry
(CMEMS names: NORTHWESTSHELF_REANALYSIS_PHYS_004_009 and
NORTHWESTSHELF_REANALYSIS_BIO_004_011)
Description
The North West shelf reanalysis was produced using the Forecasting Ocean Assimilation Model 7km
Atlantic Margin model (FOAM AMM7) which is comprised of version 3.4 of the Nucleus for European
Modelling of the Ocean (NEMO) ocean model code (Madec, 2008) coupled to the European Regional
Seas Ecosystem Model (ERSEM; Blackford et al. 2004).
The model is located on the European North-West continental Shelf (NWS), from 40°N, 20°W to
65°N, 13°E, on a regular lat-lon grid with 1/15° latitudinal resolution and 1/9° longitudinal resolution
(approximately 7km square). The model domain is shown in Figure 1 partitioned into shallow (on
shelf) and deeper (off shelf) waters. Although the domain extends beyond the shelf to include some
of the adjacent North-East Atlantic, the focus of this system is on the shelf itself and the deep water
is primarily included to ensure there is appropriate cross-shelf exchange. Thus a hybrid s-sigma
terrain following coordinate system (following Siddorn and Furner, 2013) with 51 levels is employed
in order to retain vertical resolution on the shelf. However, in order to make analysis and
visualization easier for users, the products are delivered on 24 geopotential (z-level) vertical levels
based upon the ICES standard depths.
Tidal forcing is included both on the open boundary conditions via a Flather radiation boundary
condition (Flather, 1976) and through the inclusion of the equilibrium tide. The external elevation
and depth mean velocity was determined from 15 tidal constituents taken from a tidal model of the
north-east Atlantic (Flather, 1981). With the exception of the Baltic Sea, the model was forced at its
open boundaries by temperature, salinity, sea surface height and depth integrated currents taken
from global ocean analyses. For the early part of the reanalysis (January 1984 – March 1989)
horizontal fluxes were taken from the National Oceanography Centre’s ORCA025 hindcast (Megann
et al., 2014), before switching to GloSea data (MacLachlan et al., 2014) thereafter. For the period
07/2012-06-2014 and updated GLOSEA has been used since the previous run did not cover the new
period (the Mean SSH for the NWS domain in GLOSAEA was adjust so that they both had the same
mean on July 1st 2012 thus in the mean the SSH should not have a jump though of course spatially
there will be difference). Baltic boundary conditions were taken from IOW-GETM model (see
http://getm.eu/index.php?option=com_content&task=view&id=109&Itemid=42) and from June
2014 from CMEMS-BALTICSEA-ANALYSIS_FORECAST_PHYS_003_006. Freshwater inflow into the
model from rivers and other sources was prescribed from the E-HYPE hydrological reanalysis
(Donnelly, 2013) till 2012 and then from the updated version of E-HYPE (http://hypeweb.smhi.se).
Surface forcing of 3 hourly precipitation, wind stress, pressure and shortwave fluxes were taken from
the ECMWF ERA-Interim reanalysis (Dee et al., 2011). These fluxes were then processed through the
CORE bulk forcing algorithms (Large and Yeager, 2004; Large and Yeager 2009) before being applied
to the model. The inverse barometer effect of atmospheric pressure gradients on the sea surface
height was also included. The light attenuation scheme is the RGB scheme within NEMO.
A non-linear free surface was implemented using a variable volume layer method. The short time
scales associated with tidal propagation and the free surface require a time splitting approach,
splitting modes into barotropic and baroclinic components. The bottom boundary condition includes
a log layer representation and a k-epsilon turbulence scheme is implemented with a generic length
scale (Umlauf and Burchard, 2003). The model uses a non-linear free surface, and an energy and
enstrophy conserving form of the momentum advection. It uses free slip boundary conditions. The
tracer equations use a TVD advection scheme (Zalesak, 1979). For tracer diffusion a Laplacian
diffusion scheme on geopotential surfaces is applied, whereas for momentum diffusion a mixed
Laplacian/bilaplacian scheme is used, with the Laplacian operator applied on geopotential surfaces
and the bilaplacian on model surfaces.
For the reanalysis, assimilation of Sea Surface Temperature (SST) was performed using a 3DVar
algorithm. Calculation of the assimilation increments was done using an adapted version of the
NEMOVAR (Mogensen, 2012) system. Assimilation proceeded in three steps. Firstly a one day model
forecast was performed, within which observations were compared to model output at the nearest
time-step; this is a First Guess at Appropriate Time (FGAT) system. In the second stage, observation
minus model differences were converted to SST increments by minimising a 3DVar cost function. In
minimising this function seasonally varying estimates of the observation representativity error
variance (assumed uncorrelated) and background error variance were used. The total observation
error variance was obtained by adding an estimate of the measurement error variance of each
observation to the representativity error variance. Information from observations was spread
horizontally according to lengthscales that are inversely proportional to the potential vorticity
gradient and have a maximum value of 130km. In the final stage the analysis was produced by
rerunning the model for the same day with the increments added onto the SST field using the
Incremental Analysis Update (IAU, Bloom et al., 1996) method. Increments were added into the
model down to the base of the instantaneous mixed layer, where the mixed layer depth was
determined by a 0.2°C temperature difference from the surface.
From January 1984 until October 1995 the reanalysis assimilated NOAA-AVHRR data obtained from
the Pathfinder Vn5.2 dataset (Casey et al., 2010). After October 1995 the NOAA-AVHRR feed was
switched to use the ESA-CCI (Merchant et al.,2014) version of this data; this continued until February
2010 after which a GHRSST version (see http://www.ghrsst.org) of NOAA-AVHRR data, held internally
at the UK Met-Office was used. ATSR data from the CCI project was assimilated from April 1991 until
February 2010, with UKMO data used thereafter. Also from the CCI project, METOP-AVHRR data
were assimilated from December 2006, switching to GHRSST feeds in February 2010. GHRSST data
from the AMSRE and SEVIRI instruments were assimilated from August 2006, with the AMSRE
instruments failing in November 2011. In-situ SST data were assimilated throughout the entire
reanalysis run, using ICOADS (see http://icoads.noaa.gov/) data for the majority (January 1984 until
December 2010) and data available through the Global Telecommunications System (GTS, see
(http://www.wmo.int/pages/prog/www/TEM/GTS/index_en.html) thereafter. All data were quality
controlled against the Ocean SST and sea Ice Analysis (OSTIA; Donlon et al., 2012) using the method
of Ingleby & Huddleston (2007).
The ecosystem model used World Ocean Atlas 2009 nutrients at the boundaries and river
concentration climatology for nutrients and sediment concentration. The ecosystem model is forced
by the physical model via an online coupling and is run at the same time step as the physical model,
(300s).
Both the reanalysis and control runs were completed in four segments, reflecting changes in forcing
data. For the reanalysis the runs are divided into
Run 1: 01/01/1984 to 31/03/1989
Run 2: 01/01/1989 to 31/12/2003
Run 3: 10/10/2003 to 30/06/2012
Run 4: 01/05/2012 to 30/06/2014
and for the control run
Run 1: 01/01/1981 to 31/03/1989
Run 2: 02/01/1989 to 30/03/2003
Run 3: 01/01/2003 to 30/06/2012
Run 4: 01/05/2012 to 30/06/2014.
In this document, the reanalysis product NORTHWESTSHELF_REANALYSIS_PHYS_004_009 (labelled as
NEMO-reanalysis) is assessed for 01/01/1984 to 30/06/2014. In addition, the associated control run
(labelled NEMO-control), for the same dates, and the previous hindcast, POLCOMS_REANALYSIS
covering 01/01/1964 to 31/12/2004 from the Proudman Oceanographic Laboratory Coastal Ocean
Modelling Scheme (POLCOMS), are also evaluated. Where the NEMO runs overlap, data are taken
from the earlier run until the end of that run when the assessment switches to the next run in the
series.
The biogeochemical product NORTHWESTSHELF_REANALYSIS_BIO_004_011 has been assessed for
the middle section only, 03/01/1989 to 31/12/2003. Users of the biogeochemistry product
NORTHWESTSHELF_REANALYSIS_BIO_004_011 should be aware that, owing to inter-annual drifts in
the model, there may be temporal inconsistencies at the join points between these sections as
outlined in I.2 au-dessus.
Bathymetry on the European
NorthWest Shelf
Bathymetry off of the
European NorthWest Shelf
Figure 1: FOAM AMM7 bathymetry (m) showing (left) the domain on the European NorthWest Shelf
(defined here as total depth less than 200m) and (right) the domain off the shelf.
III VALIDATION FRAMEWORK
The products assessed are temperature, salinity, currents, tidal elevation, mixed layer depth,
chlorophyll and nutrients.
The model data are compared with climatological temperature fields from the World Ocean Atlas
2009 (WOA09, http://www.nodc.noaa.gov/OC5/WOA09/pr_woa09.html) to give maps at different
vertical levels; with Pathfinder V5 Advanced Very High Resolution Radiometer (AVHRR) sea surface
temperatures integrated over the model domain; with in-situ observations from the World Ocean
Database 2013 (WOD13, http://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html),
averaged through vertical layers; and with mooring data time series at Cypris (Isle of Man), the
Western Channel Observatory (L4, E1) and from the Harwich-Rotterdam ferry.
Model salinities are compared to climatological fields from WOA09 to give maps at different vertical
levels; with in-situ observations from WOD13, averaged through vertical levels; and with mooring
data time series.
Maps of time-averaged near surface (15m deep) model currents are compared to NOAA AOML
drifter climatology (http://www.aoml.noaa.gov/phod/dac/drifter_climatology.html) and volume
fluxes through the NOOS sections in the North Sea are compared to published values, where
available.
Harmonic constants of tidal elevations for the main tidal constituents are compared to observed
values for NEMO-control.
variable
Description
Observations
Class
Metrics
temperature
Time integrated
means at depth
levels; averages
through depth layers;
time series at
mooring locations
WOA09 climatology,
WOD13 in-situ data,
Pathfinder V5 AVHRR
SST; moorings at Cypris,
Western Channel
Observatory (L4, E1) and
Harwich-Rotterdam ferry
1, 2, 3 & 4
maps, time series,
correlation, RMS
errors
salinity
Time integrated
means at depth
levels; averages
through depth layers;
time series at
mooring locations
WOA09 climatology,
WOD13 in-situ data,
moorings at Cypris,
Western Channel
Observatory (L4, E1) and
Harwich-Rotterdam ferry
1, 2, 3 & 4
maps, time series,
correlation
currents
Near surface (15m)
zonal and meridional
current climatologies
NOAA AOML drifter
climatology;
1
maps
transport
Transports through
NOOS sections in the
North Sea
Otto et al., 1990;
Prandle et al., 1996
3
time series, mean
values and
standard
deviations
tidal
constants
Mixed
depth
Maps and statistics of
errors
layer Daily/monthly Kara
MLD
Tide gauge data from the
British Oceanographic
Data Centre
1
RMS errors
MLD from EN4
temperature and salinity
profiles.
1, 4
Maps, RMS and
percent
difference
Chlorophyll
Chlorophyll
concentrations from
top model layer
Surface chlorophyll
concentration from
fluorometry at Western
Channel Observatory
(L4); GlobColour satellite
product
1, 2, 3 & 4
Time series, RMS
error, bias, maps.
Nutrients
Nitrate, silicate and
phosphate
concentrations from
top model layer
World Ocean Atlas 2009
climatology
1
maps
Table 1: Summary of metrics and observations used in the assessment
Sea level is not provided as part of this reanalysis product and therefore has not been validated in
this document. The reason for excluding sea level is that it is not possible to produce a meaningful
sea level trend product from this reanalysis because the model mean sea level is a product of the
open boundary conditions. There are four distinct model data sources used for open boundary
conditions each with their own reference sea level, internal model drifts and distinct long term
trends. Thus it would be inappropriate to infer any long term sea level trend from the reanalysis as
the signal will be dominated by the differences in the three distinct forcing datasets.
The biogeochemical variables assessed are chlorophyll and nutrients. The model data are compared
with satellite imagery for chlorophyll as well as in-situ measurements and climatological nutrient
fields from the World Ocean Atlas 2009 (WOA09,
http://www.nodc.noaa.gov/OC5/WOA09/pr_woa09.html).
IV VALIDATION RESULTS
IV.1 Temperature
Monthly mean domain averaged sea surface temperatures (SSTs, Figure 2) show broad agreement
between the three simulations considered and AVHRR SST. Mean values over the period shown are
NEMO-control 11.66°C; NEMO-reanalysis 11.74°C; POLCOMS 12.11°C and AVHRR 12.11°C. The affect
of the assimilation in NEMO is to reduce the annual range. Model SSTs are generally lower in the
winter than the AVHRR values.
Figure 2: Domain averaged SST time series for the model simulations and Pathfinder V5 AVHRR data
(top); also shown is the difference between the NEMO-reanalysis and NEMO-control runs (bottom).
The net downwards heat flux (Figure 3) integrated over the domain for 01/01/1984 to 30/06/2012
gives long term mean values of -23.0 Wm-2 for NEMO-control and -25.1 Wm-2 for NEMO-reanalysis.
For comparison, net mean fluxes from ERA-40 reanalysis range from -10 to +10 Wm-2 in the southern
North Sea, Irish and Celtic Seas and the Bay of Biscay to -70 to -50 Wm-2 south of Iceland (Kållberg et
al., 2005).
Figure 3: Domain averaged time series of net downwards heat flux (top) and the difference between
the NEMO reanalysis and NEMO control runs (bottom).
The modelled temperature fields at 0, 10, 30, 75, 300, 800 and 2000m are compared to World Ocean
Atlas 2009 (WOA09) climatology (Figure 4-Figure 6). The model data are interpolated to the
climatology vertical levels and time integrated for all whole years in the simulation (1984-2013 for
NEMO and 1964-2004 for POLCOMS). Generally, differences between the models and the
climatology are less than 2°C. All simulations underestimate the climate temperature in the
Norwegian Sea; NEMO-reanalysis shows a marked improvement over NEMO-control in this region
down to the 75m level and is also an improvement on the POLCOMS result. Except for the POLCOMS
simulation at 800m, which is colder than the climatology in the south and west of the region,
temperatures are too warm near the western boundary at all levels in all models.
In-situ data from the World Ocean Database 2013 (WOD13) are used to give contemporary
temperature comparisons (Figure 7-Figure 9). Daily mean model data for the days of the
observations are interpolated to the location of the measurement. The resulting model – observation
biases are averaged into layers for the whole of the dataset, including the first six months of 2014 for
the NEMO simulations. Biases between the models and WOD13 tend to be smaller than between the
models and climatology. Mean errors in the layers down to 300m for NEMO-reanalysis tend to be
less than 0.5°C and are smaller than NEMO-control. POLCOMS tends to be too cold down to 300m.
All models show a positive bias in the surface layer in the North Sea and are too cold in the Bay of
Biscay between 300 and 2000m.
Figure 4: Long term annual mean differences between NEMO-control temperature
(1984-2011) and WOA09 at different depth levels (model-WOA09).
Figure 5: Long term annual mean differences between NEMO-reanalysis temperature
(1984-2011) and WOA09 at different depth levels (model-WOA09).
Figure 6: Long term annual mean differences between POLCOMS temperature (19642004) and WOA09 at different depth levels (model-WOA09).
Figure 7: Mean biases between co-located values of NEMO-control temperatures (1984-2014) and
WOD13 observations in different layers (model-WOD13).
Figure 8: Mean biases between co-located values of NEMO-reanalysis temperatures (1984-2014) and
WOD13 observations in different layers (model-WOD13).
Figure 9: Mean biases between co-located values of POLCOMS temperatures (1984-2004) and
WOD13 observations in different layers (model-WOD13).
Time series of the monthly mean biases between the models and WOD13, integrated over the whole
domain into difference depth layers (Figure 10) show a general improvement in the model
temperatures from POLCOMS to NEMO-control to NEMO-reanalysis. There is no evidence of an
increase in the standard deviation of the biases (Figure 10) as time progresses.
Figure 10: Monthly mean biases and standard deviations of the biases for model temperature
compared to WOD13 observations, averaged over different depth layers.
Time series of the mean temperatures integrated over the whole domain into different depth layers
(Figure 11) show a general agreement between the model results in the surface layers down to 30m,
although the annual range of NEMO-reanalysis is smaller than NEMO-control. Between 80 and 300m,
NEMO-reanalysis temperatures exceed NEMO-control. Below 300m POLCOMS is ~1°C colder than
the NEMO simulations, with the NEMO temperatures being in better agreement with climatology
(Figure 4-Figure 6) and in-situ observations ( Figure 7-Figure 9).
Figure 11: Domain averages of temperature monthly fields as a function of time in different layers.
Using whole years of simulations only, there is a generally positive trend in SST (Figure 12) for the
region. The main exception is in NEMO-control east of Iceland and in the Faroe-Shetland Channel
where temperature decreases over 1984-2013. In the NEMO-reanalysis simulation this changes to a
generally positive trend, which is more like the AVHRR data trend. NEMO-reanalysis trends are
generally higher than for POLCOMS (these are over different time periods), but are still lower than
those derived from AVHRR data.
Figure 12: Maps of SST trend from the NEMO-control, NEMO-reanalysis and POLCOMS simulations
and from Pathfinder V5 AVHRR data (note the different colour scale).
The model temperatures are compared to four time series of observations:

water column data from Cypris Station (4.833°W, 54.092°N) from the Isle of Man Long-term
Environmental Time Series from 1984 to 2009, collected at between weekly and monthly
intervals, downloaded from the British Oceanographic Data Centre (BODC),

water column moorings E1 (4.367°W, 50.033°N) and L4 (4.217°W, 50.250°N) in the Western
Channel Observatory collected by Plymouth Marine Laboratory at between weekly and
monthly intervals and downloaded from http://www.pangaea.de; data are available for
1984, 1985 and 2002 to 2011 for E1 (Smyth et al., 2012) and 1988 to 2011 for L4 (Smyth et
al., 2011), data for 2012-2014 were downloaded from
http://www.westernchannelobservatory.org.uk,

sea surface data from the Harwich to Rotterdam Ferry between 1.416°E, 52°N and 3.916°E,
52°N collected by Cefas at weekly frequency and published as monthly mean values
(http://www.cefas.defra.gov.uk/) for 1984 to 2012.
For comparison with Cypris, E1 and L4 data, model temperatures are extracted co-located with the
date and location of each observation then monthly mean values are calculated and compared
(Figure 13-Figure 15). For the Harwich-Rotterdam ferry, monthly mean model temperatures are used
for comparison (Figure 15). At Cypris, the NEMO-reanalysis temperatures are ~0.15-0.2°C too cold
down to 10m, while NEMO-control is ~0.1° too cold. At E1, there is good agreement between NEMO
and observations above 20m but, below that depth, the model is ~0.6°C too warm. At L4, the NEMO
results are ~ 0.15-0.45°C too warm. Compared to the Harwich-Rotterdam ferry data, NEMO-control is
~0.3-0.8°C too cold and NEMO-reanalysis is ~0.5-0.9°C too cold. Correlations between the mooring
data and the model temperature are greater than 0.93 at all points.
Figure 13: Mean values of temperature profile at Cypris Station, and correlations and standard
deviations of monthly mean values.
Figure 14: Mean values of temperature profile at station E1 in the English Channel, and correlations
and standard deviations of monthly mean values.
Figure 15: Mean values of temperature profile at station L4 in the English Channel, and correlations
and standard deviations of monthly mean values.
Figure 16: Mean values of temperature along the Harwich-Rotterdam ferry route at 52°N, and
correlations and standard deviations of monthly mean values.
The innovation bias and RMS for the models compared to the assimilated data (Table 2) show the
improvement in the bias and RMS between the NEMO-control and NEMO-reanalysis simulations.
period
dataset
NEMO-reanalysis
Bias (°C)
NEMO-control
RMS (°C)
Bias (°C)
RMS (°C)
01/01/1984 –
31/03/1989
AVHRR
0.02
0.69
-0.13
0.86
in-situ
0.06
0.79
-0.03
0.91
01/01/1989 –
31/12/2003
AVHRR
0.04
0.56
0.06
0.80
AATSR
0.05
0.50
0.04
0.73
in-situ
0.05
0.55
0.12
0.81
AVHRR
0.05
0.50
0.17
0.79
AATSR
0.07
0.46
0.19
0.74
METOP
0.04
0.49
0.20
0.75
in-situ
0.03
0.46
0.25
0.84
AVHRR
0.08
0.46
NA
NA
AATSR
NA
NA
NA
NA
METOP
-0.00
0.57
NA
NA
In-situ
-0.01
0.56
NA
NA
10/10/2003 –
31/06/2012
01/05/201231/06/2014
Table 2: Innovation bias (observation – model) and RMS for surface temperature integrated over the
whole domain, divided into the three legs of the NEMO-reanalysis simulation.
IV.2 Salinity
Monthly mean domain averaged sea surface salinities (SSS, Figure 17) show broad agreement
between NEMO-control and NEMO-reanalysis, although NEMO-reanalysis results are fresher (mean
is 34.91 compared to 34.94). POLCOMS is much fresher with a mean value of 34.77 over 1984-2004.
Domain averaged net upwards water fluxes (Figure 18) for 01/01/1984 to 30/06/2012 are -9.6×10-6
kg m-2 s-1 for NEMO-control and -9.3×10-6 kg m-2 s-1 for NEMO-reanalysis.
Figure 17: Domain averaged SSS time series for the model simulations (top); also shown is the
difference between the NEMO-reanalysis and NEMO-control runs (bottom).
Figure 18: Domain averaged time series of net upward water flux for the model simulations (top);
also shown is the difference between the NEMO-reanalysis and NEMO-control runs (bottom).
The modelled salinity fields at 0, 10, 30, 75, 300, 800 and 2000m are compared to WOA09
climatology (Figure 19-Figure 21). The model data are interpolated to the climatology vertical levels
and time integrated for all whole years in the simulation (1984-2013 for NEMO and 1964-2004 for
POLCOMS). Generally, differences between the models and the climatology are less than 0.5, with
the models being too fresh. All models are too saline (up to 1 too salty) in the eastern North Sea in
the surface layers and too fresh in the Bay of Biscay at 800m.
In-situ data from WOD13 are used to give contemporary salinity comparisons (Figure 22-Figure 24).
Daily mean model data for the days of the observations are interpolated to the location of the
measurement. The resulting model – observation biases are averaged into layers for the whole of the
dataset. In the NEMO simulations, salinity is too low in several coastal regions including west of
Scotland and the Southern Bight of the North Sea and also in the Norwegian Trench; the surface layer
is too saline in the Irish Sea and west and south of the Norwegian Trench, although differences here
are smaller than in the model-climatology comparison indicating that the observed salinity over the
averaging period differs from the long term climatology. The main feature of the salinity comparison
for POLCOMS is that the modelled water around the UK coast, east of Denmark and in the surface
layers to 30m in the Norwegian Trench are too salty.
Figure 19: Long term annual mean differences between NEMO-control salinity (1984-2013) and
WOA09 at different depth levels.
Figure 20: Long term annual mean differences between NEMO-reanalysis salinity (1984-2013) and
WOA09 at different depth levels.
Figure 21: Long term annual mean differences between POLCOMS salinity (1964-2004) and WOA09 at
different depth levels.
Figure 22: Mean biases between co-located values of NEMO-control salinities (1984-2014) and
WOD13 observations in different layers.
Figure 23: Mean biases between co-located values of NEMO-reanalysis salinities (1984-2014) and
WOD13 observations in different layers.
Figure 24: Mean biases between co-located values of POLCOM salinities (1984-2004) and WOD13
observations in different layers.
Time series of the mean biases between the models and WOD13, averaged into monthly means and
integrated over the whole domain into different depth layers (Figure 25) show a general
improvement in the model salinity from POLCOMS to NEMO. There is little difference between
NEMO-control and NEMO-reanalysis. There is no evidence of an increase in the standard deviation of
the biases (Figure 25) as time progresses.
Figure 25: Mean biases and standard deviations of the biases of model salinity compared to WOD13
observations, averaged over different depth layers.
Time series of the mean salinity integrated over the whole domain into different depth layers (Figure
26) show that generally NEMO-reanalysis is fresher than NEMO-control and POLCOMS is fresher than
both NEMO runs. One exception is in the 300-800m layer from 1990 onwards when the salinity in the
NEMO simulations reduces by ~0.1. The NEMO salinity values are in better agreement with
observations (Figure 25) than the POLCOMS results.
Figure 26: Domain averages of salinity monthly fields as a function of time in different layers.
The trends in SSS in NEMO-reanalysis and NEMO-control are similar (Figure 27). Away from the
coastal regions, the trend is towards decreasing salinity, which is especially large in the Norwegian
Sea ~-0.02 yr-1. In POLCOMS the trend is an increase in salinity in the Norwegian Sea but a reduction
elsewhere, including in coastal regions.
Figure 27: Maps of SSS trend from the NEMO-control, NEMO-reanalysis and POLCOMS simulations.
As for the temperature data, the model salinities were compared to four time series of observations
at Cypris (surface salinity only), E1 and L4 in the English Channel and along 52°N on the Harwich to
Rotterdam ferry. At Cypris, NEMO is more saline than the observations at the surface, with a mean
value for NEMO-control of 34.68 and for NEMO-reanalysis of 34.55, while the observations have a
mean of 34.17; for both NEMO-control and NEMO-reanalysis, correlations between the monthly
mean model and observed values are 0.7 and standard deviations of the bias are 0.1. At E1 (Figure
28) below 30m, NEMO-control has good agreement with the observed mean profile; near the surface
the model is too saline. Near the surface, NEMO-reanalysis is a better approximation to the
observations but is too fresh throughout the profile. At L4 (Figure 29), except at the surface where
both models are too saline compared to the mooring observation, the mean profile from NEMOcontrol is too saline whilst NEMO-reanalysis is close to the observed but too well mixed in the
vertical. On the Harwich-Rotterdam ferry section (Figure 30), the models are on average too saline by
~0.4 in the west and too fresh by ~1.9 in the east. Correlations between monthly mean model and
observed salinities are low at the English Channel stations E1 and L4, with values of up to 0.5 at E1
but negative correlations at L4. Correlation with the ferry data is higher with values ranging from 0.3
to 0.8.
Figure 28: Mean values of salinity profile at station E1 in the English Channel, and correlations and
standard deviations of monthly mean values.
Figure 29: Mean values of salinity profile at station L4 in the English Channel, and correlations and
standard deviations of monthly mean values.
Figure 30: Mean values of salinity along the Harwich-Rotterdam ferry route at 52°N, and correlations
and standard deviations of monthly mean values.
IV.3 Currents
Near surface climatologies for two components of currents averaged over all complete years of the
simulations (Figure 31) show agreement between the model data sets, all models showing the
clockwise circulation around the north of the Faroe Islands and southwards into the Faroe-Shetland
Channel; the northwards flow on the Shetland side of the Faroe-Shetland Channel turning south into
the North Sea; and the Norwegian Coastal Current. These features are in broad agreement with the
NOAA AOML drifter climatology. The POLCOMS currents have stronger eddy fields in the Bay of
Biscay compared to the NEMO simulations.
NEMO-control (1984-2013)
NEMO-reanalysis (1984-2013)
POLCOMS (1964-2004)
NOAA AOML drifter climatology
Figure 31: Near surface mean currents for NEMO-control, NEMO-reanalysis and POLCOMS, also
NOAA AOML drifter climatology.
IV.4 Tidal analysis
For sea level elevations, harmonic analysis of the dominant tidal constituents is compared against
observations. Figure 32 show the amplitude and phase errors for M2. Figure 32 shows the
comparison spatially. The amplitude is underestimated in the Irish Sea and Southern Bight and
overestimated in large tidal areas such as the Bristol Channel, as also seen in Figure 33. The phase
error is particularly large in the Southern Bight and German Bight. The RMSE for amplitude and phase
for the constituents M2, S2, K2, N2, K1, P1 and O1 are shown in Table 3. Overall the tides are
reasonably well represented but caution must be applied particularly with regards to tidal phase in
areas such as the Southern and German Bights.
Figure 32: M2 amplitude (left) and phase (right) error for NEMO-control run.
Figure 33: M2 amplitude and phase versus observations
Constituent
RMSE Amp. (cm)
RMSE Phase (Deg)
M2
11.60
15.6
S2
4.50
15.8
K2
1.46
13.1
N2
3.39
20.7
K1
2.10
18.8
P1
0.99
19.7
O1
1.91
19.9
Table 3: RMSE amplitude and phase of control run tidal constituents against observations.
IV.5 Transport
Mean transports though the NOOS sections in the North Sea (Figure 34) are compared to available
observations (Table 4). All models simulate the inflow to the North Sea through the Shetland North
section in good agreement with the observation (Otto et al., 1990). The outflow from the North Sea
through the Sojgnesjoen section is also simulated with the NEMO results being slightly smaller than
POLCOMS and the observed value. In agreement with observations, the models show an inflow to
the North Sea through the Shetland South section, with the POLCOMS transport being larger than
the observed and the NEMO transports being smaller than observed. The NEMO transport into the
North Sea through the Dover Strait is in good agreement with observations (Prandle et al., 1996),
while POLCOMS has a weak outflow here.
For eight of the NOOS sections, the net flux through each section (Figure 35 and Figure 37) and two
components of transport in both directions normal to the section (Figure 36 and Figure 38) for
POLCOMS and NEMO-reanalysis are shown; NEMO-control being similar to NEMO-reanalysis is not
shown.
Figure 34: Locations of NOOS sections for volume flux calculations.
no
section
positive
flux
direction
observed
mean
(Sv)
NEMO-control
mean
(Sv)
std
(Sv)
NEMO-reanalysis
mean
(Sv)
std
(Sv)
POLCOMS
mean
(Sv)
std
(Sv)
1
Shetland North
N
-0.6
-0.52
0.21
-0.55
0.22
-0.55
0.21
2
Sojgnesjoen
N
1.8
1.25
0.50
1.28
0.50
1.61
0.44
3
Shetland South
NW
-0.3
-0.11
0.04
-0.11
0.04
-0.48
0.22
4
Orkney
N
-0.93
0.37
-0.94
0.36
5
Utsira
N
1.10
0.43
1.10
0.42
6
Lista
NW
0.36
0.24
0.35
0.24
7
Aberdeen
N
-0.33
0.18
-0.33
0.18
8
Hantsholm
N
0.33
0.22
0.32
0.22
9
Okso
NE
-0.01
0.01
-0.01
0.01
10
Flamborough Head
N
-0.01
0.06
-0.01
0.06
11
Terschelling
NE
0.14
0.09
0.14
0.09
12
Noordwijk
N
0.06
0.06
0.06
0.06
13
Dover Strait
NE
0.08
0.06
0.07
0.06
-0.01
0.06
14
Cherbourgh
E
0.08
0.06
0.07
0.06
15
Plymouth
E
-0.05
0.07
-0.05
0.07
-0.10
0.49
16
Rosslare
N
0.05
0.06
0.05
0.06
-0.08
0.08
17
Dublin-Holyhead
N
0.09
0.06
0.08
0.06
-0.01
0.08
18
Larne
N
0.06
0.05
0.06
0.05
-0.00
0.08
19
Rottumerplaat
E
0.10
0.07
0.10
0.07
20
Sylt
N
0.08
0.06
0.08
0.06
21
Torungen-Hirtshals
NE
0.00
0.01
0.00
0.01
22
Skagerrak
E
-0.01
0.01
-0.01
0.01
-0.20
0.08
23
Kategatt
N
-0.01
0.00
-0.01
0.00
0.094
Table 4: For the NOOS sections (Figure 34) means and standard deviations of monthly mean volume
fluxes from 01/01/1984 to 30/06/2012 for the NEMO control and reanalysis simulations and
01/01/1964 to 31/12/2004 for the POLCOMS simulation (for available sections). Volume fluxes
derived from observations are from Otto et al. (1990) for sections 1, 2 and 3 and from Prandle et al.
(1996) for section 13. The directions of positive flux are denoted by N – northwards, E – eastwards, W
– westwards, NE – northeastwards and NW – northwestwards.
Figure 35: Monthly averaged net transports through selected NOOS sections (Figure 34) for the
POLCOMS simulation, in Sverdrups.
Figure 36: Two components of monthly averaged transports through selected NOOS sections (Figure
34) for the POLCOMS simulation, in Sverdrups.
Figure 37: Monthly averaged net transports through selected NOOS sections (Figure 34) from the
NEMO-reanalysis simulation, in Sverdrups.
Figure 38: Two components of monthly averaged transports through selected NOOS sections (Figure
34) for the NEMO-reanalysis simulation, in Sverdrups.
IV.6 Mixed layer depth
V
A note on the data processing
The CMEMS NWS RAN output a modified version of the Kara MLD (Kara et al. 2000), using a with the
reference depth at 3m instead of 10m. These were available as 25 hourly means (for each day 25
hourly instantaneous MLD estimates were averaged, including the midnight at the beginning and end
of the period). This was done to approximately remove the tidal cycle. These are released as the daily
product. This is a less appropriate strategy for when averaging over the month, when the tidal cycle
is arguably less important, as it includes a “night-time” bias. However, 24 hourly mean files were not
output for by this version of the RAN, and offline calculation of the MLD was not possible, and so the
monthly mean files are simply the average of the 25 hour mean files for each month.
We have limited number of hourly mean MLD and so have been able to quantify this “night-time”
bias. Spatial maps of the bias, rms and percent difference between the (monthly and daily) means
when calculated from 25 or 24 hour means are presented in Figure 39 for an exemplar year (1990).
The time series of these differences for the NWS are presented in Figure 40.
Figure 39 Bias (upper row), RMS (middle row) and percent difference (bias/24hour mean; lower
row) from producing monthly (left column) and daily (right column) means from daily 25 hour
means rather than daily 24 hour means.
Figure 40 Time-series of the 25 and 24 hour mean MLD averaged over the shelf (left), the
difference between these (middle), and the percent difference (right). The upper panel shows the
daily mean files (from 1st January to 31st December 1990) , and the lower panel shows the monthly
mean files (from January to December 1990).
Given the small scale of these quantified differences, we consider this use of daily and monthly
means as calculated from 25 hour means sufficient for the NWS.
VI
A note on the observations
In order to validate the CMEMS RAN MLD we have use MLDs calculated from the quality controlled
temperature and salinity profile database, EN4 (Good et al. 2013). These MLD are similar, but
different from, the Kara MLD (referred to as EN4 MLD). In order to assess whether the EN4 MLD
were fit for purpose to validate the CMEMS RAN Kara MLD, we have used the same EN4 MLD
algorithm to the 25 hour mean 3d temperature and salinity fields of CMEMS RAN and compared
them to the CMEMS RAN Kara MLD. This was done for each day of an exemplar year (2000).
For each day, we compare the two MLD products on the shelf. We present a Taylor diagram (Figure
41) to show the spatial agreement, with time series of the shelf wide MLD, bias and the time-series
of the relative standard deviation and correlation (as shown on the Taylor Diagram). We find that the
correlation is generally above 0.95, and the shelf mean bias is generally < 1m, and there is greatest
disagreement from May to October. We then look at the spatial pattern of the bias averaged over
these months (Figure 42). When looking at these maps, we find there is a good agreement in the
interior of the shelf, with the greatest differences in the Shetland Shelf (north of the North Sea), the
Irish Shelf (west of Ireland) and in the outer Celtic Sea.
Overall we find that there is a relatively good agreement between the two methods of calculating
MLD on the shelf and conclude that the EN4 MLD’s are fit for the purpose of validating the CMEMS
NWS MLDs, however recommend that for later versions, a consistent processing of the EN4 MLDs is
undertaken.
Figure 41 Comparison of simulated observed (EN4) MLD with modelled MLD. Taylor diagram
showing the spatial agreement for each day of the (exemplar) year (2000), with time series of
shelf-wide mean (modelled and observed) MLD, bias and Taylor statistics (relative standard
deviation and correlation).
Figure 42 Comparison of simulated observed (EN4) MLD with modelled MLD, averaged from May –
October (inclusive), showing the mean modelled and observed MLD fields, the model-observed
bias and the percent difference.
VII MLD Evaluation
Given the use of 25 hour means, and the EN4 MLD, and the relative paucity observations to evaluate
the CMEMS NWS RAN MLD, we now make a careful evaluation.
For each EN4 observed MLD, we extract the modelled MLD from the same grid-box, and same day
(termed a data pair). The EN4 MLD data does not return a value when the water column is mixed,
and so there are no observation-model MLD data pairs regions that are observed to be mixed (such
as the southern North Sea and English Channel). From Figure 41 we identify the predominantly
stratified months as being May – October, and so we focus our validation of this period.
We initially compare the spatial patterns of the co-located modelled and observed MLD for the
stratified months (May – October: Figure 43). It is clear from Figure 43 that there is much greater
data density in the North Sea, and outer shelf regions, than in the Celtic and Irish Sea, and in the
English Channel.
There are no clear spatial patterns in the bias or rms. There is a slightly more positive modelobservations bias (model MLD too deep) in the eastern North Sea compared to the rest of the
domain, and a negative (model MLD too shallow) in the sparsely sampled Celtic Sea.
Figure 43 Spatial patterns of the co-located modelled and observed MLD for the stratified months
(May – October). For each EN4 observed MLD, the modelled MLD for the same day and grid box is
extracted. The average for these data (between May and August) for each grid box is shown here
(upper left and right respectively). Below, we show the statistics of these data (for each grid box):
the mean bias (model minus observation), rms and percentage difference (model minus
observation bias divided by observation), lower row, from left to right respectively.
We then look at all the model observation MLD data pairs (in terms of model minus observation
bias), and plot as a function of time and seasonal cycle (Figure 44). The data appears to have
stationarity (in terms of the mean and variance of the bias) over the duration of the CMEMS NWS
RAN, with no apparent trend in either the mean or the variance. There is a clear seasonal cycle in the
number of observations, although this may be accounted for the exclusion of observed mixed
profiles. The biases appear to be positively skewed, and so the apparent mode of the data (e.g. ~-10
m - 0 m between day-number 150 – 250) does not necessarily reflect the mean.
Figure 44 Timeseries of all model-observations bias pairs of data, plotted as a function of year, and
day of year (upper left and right panels respectively). The lower panels show the same as the
panels above, but zoomed in to give mode detail. The colouring gives a qualitative indication of
data density.
We now look at the model observation MLD data pairs (from the shelf) in a more quantitative way.
For each month, we calculate the Taylor statistics (correlation, relative standard deviation, rms), the
mean bias, mean observed and modelled MLD, and the number of data pairs (Figure 45). The Taylor
diagram shows, with the exception of June – August, most correlations are greater than 0.6, and the
model variance is generally greater than that of the observations (relative standard deviation > 1.).
The months of June-August have much lower correlations (0.586, 0.442 and 0.361 respectively), but
have similar modelled and observed variance, and much lower model minus observations biases.
Figure 45 Quantitative analysis of the observed (EN4) and modelled (amm7) MLD data pairs. The
Taylor diagram (left panel) shows agreement of the spatial patterns of modelled and observed
MLD. The panels on the right hand side show the seasonal cycle of the mean MLD (from the sparse
modelled and observed data pairs), the model minus observations bias, the correlation and
relative standard deviation (repeating the data from the Taylor diagram) and the number of data
pairs.
When we look at individual validation regions (the North Sea, the Celtic seas (the Celtic and Irish Seas
and the English Channel) and the outer shelf (the Irish and Shetland shelf)) for the stratified months
(Figure 46), it can be seen that while the correlations are particularly low for the Celtic Seas relative
to the North Sea, there are far fewer observations, and so the North Seas correlations are fairly
similar to that of the whole shelf.
Figure 46 Model (amm7) observation (EN4) data pairs, for May to October (from left to right), for
the 3 main validation regions: the North Sea; the Celtic seas (the Celtic and Irish Seas and the
English Channel); and the outer shelf (the Irish and Shetland shelf). The colour of the points show
the data density. Each panel include the relative standard deviation (rsd), correlation (r), rms,
correlation significance (p), the mean bias, and the number of data points (cnt).
VII.1 Chlorophyll
The model output was compared to observations from the Western Channel Observatory L4 buoy
(Figure 47). This data consists of approximately weekly measurements from the surface of
chlorophyll concentration derived from fluorometric techniques from 1992 until 2003. The model is
capturing the timing of the seasonal variation in surface chlorophyll well and also the magnitude of
winter minimums and spring maximums although the maxima in 1997 and 1999 are under estimated.
This analysis will be extended for the final section of the reanalysis and fore earlier periods where
data is available.
Figure 47: Surface chlorophyll concentration (mg/m3) at L4 buoy (x) and model output (-). RMS error
is 1.93 and model bias is 0.24.
The model output was also compared to GlobColour satellite imagery products for chlorophyll
concentration available from 1998 for case 1 waters which are summarised in Table 5. The biases are
generally low with the best performance in the Irish Sea and the English Channel. For all regions the
model is over estimating chlorophyll producing negative biases. Figure 48 shows a time-series of the
errors for the whole domain and indicates that over the period analysed there is no significant
increase in errors and the model is stable.
Area Name
Mean error (model-obs)
RMS error
Full Domain
-0.143
0.667
Continental shelf
-0.167
0.631
Off-shelf
-0.135
0.679
Norwegian Trench
-0.259
0.659
Northern North Sea
-0.197
0.765
Southern North Sea
-0.123
0.512
English Channel
-0.073
0.419
Irish Sea
-0.068
0.468
South Western Approaches
-0.160
0.685
North Western Approaches
-0.141
0.613
Table 5: Regional statistics for log10 of chlorophyll-a (mg/m3) concentration against the Globcolour
product data for the period January 1998 – June 2002
Further analysis and comparison with satellite imagery will be carried out for the later parts of the
reanalysis. This analysis began with the earliest available satellite data from the SeaWifs sensor and
any analysis of earlier periods will depend on the quality and coverage of available observations.
Figure 48: Log10 Chlorophyll concentration (log10(mg/m3)) mean error (dotted line), RMS error (solid
line) for the full model domain from January 1998 to June 2002.
Figure 49: Mean surface chlorophyll for June 1998-2003. GlobColour imagery left, model output right.
Figure 49 shows surface chlorophyll concentration maps of GlobColour imagery (left) compared to
model output (right) for the month of June highlighting the region of over production in the northwest of the model domain. This figure also gives an indication of the inter-annual variability of the
model results.
VII.2 Nutrients
There are few available in-situ observations available for verification so an initial comparison to
World Ocean Atlas climatology is made below. Figure 50, Figure 51 and Figure 52 show World Ocean
Atlas nutrient data from February when nutrient concentrations are highest and August when they
are lowest. These are shown next to monthly mean output from the model from 1990 at the
beginning of the run and 2003 at the end. Figure 50 shows that the February (winter) and August
(summer) surface nitrate compares well to the climatological data, however it is noted that there is a
decline in winter nitrate concentration between 1990 and 2003. There is also an increase in the
nitrate concentration in both February and August in coastal regions and the Irish Sea. This pattern is
also seen in plots of phosphate in Figure 51. Figure 52 again shows a similar trend in decreasing
surface concentrations with an even more marked increase in coastal concentrations. This section of
the reanalysis is the longest and nutrient drifts will be compared to the other sections in future
updates.
Figure 50: Surface monthly nitrate concentration (mmol/m3) for February (top row) and August
(bottom row) from World Ocean Atlas (left), model output from 1990 (middle) and model output from
2003 (right).
Figure 51: Surface monthly phosphate concentration (mmol/m3) for February (top row) and August
(bottom row) from World Ocean Atlas (left), model output from 1990 (middle) and model output from
2003 (right).
Figure 52: Surface monthly silicate concentration (mmol/m3) for February (top row) and August
(bottom row) from World Ocean Atlas (left), model output from 1990 (middle) and model output from
2003 (right).
VIII QUALITY CHANGES SINCE PREVIOUS VERSION
The NEMO reanalysis product NORTHWESTSHELF_REANALYSIS_PHYS_004_009 has been compared
to the POLCOMS hindcast product NORTHWESTSHELF_REANALYSIS_PHYS_004_005 for the
Northwest European Shelf. The main changes in quality are:
Temperature biases have improved throughout the water column, with the exception of the 30-80m
layer in the southern North Sea.
Salinity biases have larger magnitudes in the coastal regions of the southern North Sea but have
improved in the Norwegian Coastal Current and around the UK coast.
The eddy climate has reduced in the Bay of Biscay.
Volume transports through NOOS sections have changed but still compare well with the limited
observations that are available.
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