atlantic ibi -iberian biscay irish- biogeochemical multi- year ......3.0 12/’02/2018 all update...

© EU Copernicus Marine Service – Public / 109

Atlantic IBI -Iberian Biscay Irish- Biogeochemical Multi-year Product

IBI_REANALYSIS_BIO_005_003

Issue: 3.2

Contributors: McGovern J.V., Dabrowski T., Gutknecht E., Lorente P., Reffray G., M.G Sotillo

Approval Date by Quality Assurance Review Group : 29/05/2020

QUID for IBI Biogeochemical Multi-Year Product


Ref: CMEMS-IBI-QUID-005-003

Date : 3 April 2020

Issue : 3.2


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 acceptance

QuARG 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 validate the IBI BIO REA update at CMEMS V2

T. Dabrowski, G. Reffray, C. Perruche, E. Gutknecht, M.G. Sotillo

Enrique Álvarez Fanjul

2.1 16/03/2016 All Expansion of descriptive text following the Design Review in February 2016

T. Dabrowski, G. Reffray, C. Perruche, E. Gutknecht, M.G. Sotillo

Enrique Alvarez

3.0 12/’02/2018 All Update with product quality results from the V4 IBI BIO MY product.

Bowyer P., Dabrowski T., Gutknecht E., Lorente P., Reffray G., M.G Sotillo

Enrique Álvarez & Angelique Melet

3.1 15/01/2019 Update of the MY Product. Extension of the temporal coverage until year 2017. Including re-run of years 2014 to 2016.

Gutknecht E.

3.2 03/04/2020 Extension of the temporal coverage until year 2018. Including re-run of years 2014 to 2017.

New Interim temporal extension.

McGovern J.V., Dabrowski T., Gutknecht E., Lorente P., Reffray G., Roland Aznar, M.G Sotillo




Date : 3 April 2020

Issue : 3.2


TABLE OF CONTENTS

I EXECUTIVE SUMMARY ............................................................................................................................ 4

I.1 Products covered by this document ........................................................................................................... 4

Summary of the results .................................................................................................................................... 5

I.2 Estimated Accuracy Numbers .................................................................................................................... 7

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.1.1 Regional differences: chlorophyll ............................................................................................... 19

IV.2 Nitrates .................................................................................................................................................... 29

IV.3 Phosphate ................................................................................................................................................ 37

IV.4 Silicate ..................................................................................................................................................... 42

IV.5 Oxygen ..................................................................................................................................................... 48

IV.6 Primary productivity ............................................................................................................................. 54

IV.7 Euphotic layer depth .............................................................................................................................. 60

V In situ data ................................................................................................................................................... 66

V.1 IBI comparison with in-situ data from Bio-Argo .................................................................................. 66

V.2 Cruise data ............................................................................................................................................... 73

V.3 Ferrybox data ........................................................................................................................................... 86

VI QUALITY CHANGES SINCE PREVIOUS VERSION ............................................................................ 89

VI.1 Chlorophyll ............................................................................................................................................ 89

VI.2 Carbon system ..................................................................................................................................... 91

VII Temporal extensions ............................................................................................................................... 94

VII.1 Extension to the year 2017 ............................................................................................................... 95

VII.2 Extension to the year 2018 ............................................................................................................. 100

VII.2.1 ERA atmospheric forcing ......................................................................................................... 103

VIII References ............................................................................................................................................. 108




Date : 3 April 2020

Issue : 3.2


I EXECUTIVE SUMMARY

I.1 Products covered by this document

The product assessed in this document is referenced as: IBI_REANALYSIS_BIO_005_003

The IBI_REANALYSIS_BIO_005_003 product, in its V4 version, 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 01/1992 – 12/2016. The product has been extended up to 12/2018. The qualification of this extension is added to the end of the document (see Section 0).

The product is delivered through two different datasets: dataset-ibi-reanalysis-bio-005-003-monthly and dataset-ibi-reanalysis-bio-005-003-daily. Both datasets provide the IBI daily and monthly multiyear information at 1/12° resolution, covering the the IBI Service Domain (detail in magenta 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 information. However, the biogeochemical model application 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. For the V4 qualification, maps presented in this document show the IBI Service Domain, in addition all statistics, metrics and the EANs, Estimated Accuracy Numbers, end-user oriented were computed for the IBI Service Domain on its regular model grid, where information is available to CMEMS users.




Date : 3 April 2020

Issue : 3.2


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 IBI REA simulation

(black).

Summary of the results

The quality of the biogeochemical product delivered has been assessed using a 25-year time period (years 1992-2016). The headline results for each of the variables assessed are as follows:

Chlorophyll: At sea surface, modelled chlorophyll fields show generally good agreement with satellite data, with mean concentrations somewhat higher than for the observations, and in general more pronounced in productive regions. The timing of the spring phytoplankton bloom is consistent with satellite observation, but the model is more productive. The amplitude of the seasonal cycle is too strong: the model overestimates chlorophyll concentration at the peak bloom, returning to approximately 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.

Nitrates: The annual cycle of nitrate concentration is quite well captured by the model. Concentration of nitrates in the model is smaller than WOA climatology, especially in the coastal productive areas, subtropical gyre and Mediterranean Sea. Model performance deteriorates markedly in the summer months when compared to the remaining months and the model generally overestimates nitrates in the summer. The model represents vertical structure well, with some deviation at the base of the mixed layer and in shallow seas.




Date : 3 April 2020

Issue : 3.2


Phosphates: Overall the model displays similar spatial pattern to the World Ocean Atlas Climatology over the IBI region, agreeing well in the open ocean at higher latitudes and underestimate in in the sub tropical gyre and Mediterranean Sea, as nitrates. The mean concentration of phosphates is slightly too low at sea surface as compared to climatology, with a mean concentration of 0.19 vs 0.22 mmol/m3

for the climatology.

Silicates: The concentration of silicates is too low at sea surface over the IBI area, (1.88 vs 2.09 mmol/m3.Overall, it displays similar spatial pattern to the World Ocean Atlas Climatology, but there are significant regional differences. Lower surface model concentrations are seen to the South of Ireland and off Morocco, while higher model concentrations exist in the Northwest part of the domain, in Biscay and the Mediterranean Sea.

Dissolved oxygen: Oxygen presents a very good agreement at sea surface in comparison with World Ocean Atlas climatology, with a bias of -3.2 mmol/m3 (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 well in the model compared to the product derived from the satellites (Globcolour). Over the IBI domain, the model overestimates euphotic depth by 8 m, which represents approximately 15% 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, the subtropical gyre and possible the shallow Northern Seas, where model overestimates the euphotic depth. The model generally overestimates in Autumn.

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.




Date : 3 April 2020

Issue : 3.2


I.2 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 provides essential statistics on the delivered biogeochemical variables, obtained from long-term comparisons with reference information based on both satellite and climatology data sources.

Table 1: Mean and standard deviation (in brackets) over the IBI Service domain at sea surface.

Model values averaged over the years 1993-2016 (Oxygen, Nitrate, Phosphate and Silicate). Model values for 1998-2016 were used for Euphotic layer depth and Chlorophyll, and 2002-2016 for net

primary productivity.

Observations: WOA13a climatology (Oxygen, Nitrate, Phosphate and Silicate). Satellite derived data (Chlorophyll, Euphotic depth, and Primary productivity).

Variable Observations Model

Chlorophyll , mg m-3 0.45 (0.59) 0.50 (0.72)

Dissolved oxygen, mmol m-3

249.20 (19.18) 252.43 (19.00)

Nitrates mmol m-3 2.44 (3.08) 1.82 (2.30)

Phosphates mmol m-3 0.22 (0.18) 0.19 (0.18)

Silicates mmol m-3 1.88 (1.49) 2.09 (1.10)

Euphotic depth, m 56.04 (19.94) 64.23 (21.20)

Net primary productivity,

mgC m-2 d-1

824.38 (694.99) 371.15 (275.88)




Date : 3 April 2020

Issue : 3.2


Table 2: Root Mean Square Error (mod-obs)2 √< (obs-mod)2 >, Mean Error <

(obs-mod) >, Percentage of Bias (%) = |Mean Error /Mean Obs|, and Correlation is R – coefficient of correlation, calculated at sea surface with model fields averaged over the years: 1993-2016

(Oxygen, Nitrate, Phosphate and Silicate, 1998-2016 for Euphotic layer depth and Chlorophyll, and 2002-2016 for net primary productivity.

Variable RMS difference Mean Error Percent Bias (%)

Correlation

Chlorophyll , mg m-3 0.51 0.05 12 0.71

Dissolved oxygen, mmol m-3

6.36 3.23 1 0.94

Nitrates mmol m-3 2.04 -0.63 26 0.75

Phosphates mmol m-3

0.1 -0.02 11 0.83

Silicates mmol m-3 1.31 0.21 11 0.53

Euphotic depth, m 11.18 8.19 15 0.85

Net primary productivity,

mgC m-2 d-1

543.2 -453.23 55 0.69




Date : 3 April 2020

Issue : 3.2


II 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 PISCES v2 (Aumont et al., 2015), part of NEMO 3.6 modeling platform, is a model of intermediate complexity. It considers 24 prognostic variables. There are five limiting nutrients for phytoplankton growth: nitrate and ammonium, phosphate, silicate and iron. Phosphate and nitrate + ammonium are linked by a constant Redfield ratio C/N/P (122/16/1; Takahashi et al., 1985) in all organic compartments of PISCES. The model distinguishes two phytoplankton size compartments (nanophytoplankton and diatoms), for which prognostic variables are total biomass in carbon, iron, chlorophyll, and silicon (the latter only for diatoms), and hence the Fe/C, Chl/C, and Si/C ratios are variable and then prognostically predicted by the model. Two zooplankton size classes (microzooplankton and mesozooplankton) are considered, with constant ratios. Total biomass in C is thus the only prognostic variable for zooplankton. 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 content of the particles is prognostically computed. Next to the three organic detrital pools, calcium carbonate (calcite) and biogenic silicate particles are modeled. Besides, the model simulates the carbonate system (dissolved inorganic carbon and total alkalinity) and dissolved oxygen.

Even if PISCES was initially designed for global ocean applications, 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 at global and regional scales, at low and high spatial resolutions as well as for short-term and long-term analyses (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; Gutknecht et al.; 2016). PISCES is also the biogeochemical model used for the IBI Analysis and Forecast products (IBI_ANALYSIS_FORECAST_BIO_005_004), the Global Ocean Analysis Product (GLOBAL_ANALYSIS_BIO_001_014) and the Non-Assimilative Hindcast Product (GLOBAL_REANALYSIS_BIO_001_018), developed and produced at Mercator Ocean for delivery to CMEMS.




Date : 3 April 2020

Issue : 3.2


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), with the same 1/12° horizontal 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 QUICKEST scheme (Leonard, 1979) with limiter of Zalezak (1979).

The simulation started on January 1st, 1992 up to December 24,2018. The biogeochemical model is initialized with a monthly climatology build from the Global Ocean Analysis Product (GLOBAL_ANALYSIS_BIO_001_014) at ¼° horizontal resolution for the same starting month. The climatology is built using years 2010 to 2015. Open boundary conditions come from this same climatology on a monthly basis.

Other boundary fluxes account for external supply of nutrients (N, P, Si, Fe and DIC) from three different sources: atmospheric dust deposition, rivers, and marine sediment mobilization. For more details on external supply of nutrients, please refer to Aumont et al. (2015).

Following the CMEMS recommendations, the carbon cycle has been improved through the condition at the surface boundary of the CO2 partial pressure taking into account the anthropogenic effects.

Two regional adaptations have been considered. First one concerns the vertical sedimentation. By default, PISCES considers ~40% loss at the sediments. But literature shows that strong tidal currents prevent organic matter (POM, BSi, CaCO3) from settling on the bottom and being stored in sediments in the English Channel and North Sea. So, taking into account this regional specificity, 0% sediment losses was considered. Second adaptation deals with nutrient input from rivers. To have a more realistic system, two types of inputs are considered. The natural inputs are injected into the model in the form of surface flow in the river plumes but also along the coastline, from the annual ½ ° Global News 2 climatology which reproduces a realistic hydrology for the year 2000. Additional (anthropogenic) inputs of NO3 and PO4 are introduced into the system as lateral fluxes (similar to an open boundary) prescribed at the river sources points. These additional NO3 and PO4 come from rivers monitored and listed by the European Environment Agency (annual average). For the other variables, a reminder to the initial conditions is made.




Date : 3 April 2020

Issue : 3.2


III VALIDATION FRAMEWORK

We assess the system performance and the associated product quality by comparing modeled biogeochemical fields (sections and maps) with data or climatology (CLASS1) when they are available. These visual comparisons are done using the time average of the biogeochemical fields over the years 1993 to 2016 for oxygen, nitrate, phosphate and silicate; 1998-2016 for euphotic layer depth and chlorophyll; and 2002-2016 for net primary productivity.

The validation methodology and metrics classification are 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

Reference observational dataset

Chlorophyll a IBI CLASS1 ESA CCI OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065

Chlorophyll a IBI CLASS3 ESA CCI OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065 Globcolour (ACRI) 1 km AVW

Chlorophyll a IBI CLASS2 ESA CCI OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065 Globcolour (ACRI) 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)

Primary production

IBI CLASS1 VGPM, Oregon State University

Primary production


Primary production


Euphotic layer IBI CLASS1 Globcolour (ACRI) 1km AVW






Date : 3 April 2020

Issue : 3.2


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 said to be excellent for PB<10, very good for 10<PB<20, good for 20<PB<40, and poor for PB>40.

Taylor diagrams (Taylor, 2001) are also included in this report and these present the statistics computed for monthly and annual climatology. ‘IBI REA’ on Taylor diagram refers to the annual averages and all monthly climatology 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:

𝐶𝑒𝑛𝑡𝑒𝑟𝑒𝑑 𝑅𝑀𝑆𝐷 = √∑[(𝑜𝑏𝑠 − 𝑚𝑒𝑎𝑛(𝑜𝑏𝑠)) − (𝑚𝑜𝑑 − 𝑚𝑒𝑎𝑛(𝑚𝑜𝑑))]2

𝑁




Date : 3 April 2020

Issue : 3.2


IV VALIDATION RESULTS

This biogeochemical system is evaluated by systematically comparing model fields to climatology (Oxygen, Nitrate, Phosphate and Silicate) or monthly satellite-derived data (Euphotic depth, Chlorophyll, and net primary productivity). 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.

A variety of in situ data is available. Station data collected by research cruises is available throughout the hindcast period. Argo float data is available from 2011, including oxygen measurements; on a small subset of these floats (bio argo), nitrate and chlorophyll measurements are available. In addition, towed sensors are available, and routine underway measurements from ships of opportunity; here results from the ferrybox programme are used.

IV.1 Chlorophyll

Figure 2 shows a comparison of averaged surface chlorophyll over the period 1998-2016 between the REA model and satellite (ESA-CCI) data. Model is masked according to the availability of the satellite data, which is limited, especially in the North part of the domain and in winter. Highest concentrations are found along the coasts, in the shallower Northern Seas, along the Western facing Atlantic coasts, including upwelling areas off Iberia and Morocco. The model slightly overestimates chlorophyll in these productive areas, except Morocco. Lowest concentrations are found in the subtropical gyre to the South part of the domain.

Figure 2. Concentration of chl-a: mean over the years 1998-2016 at sea surface (mg Chl.m-3); (left) IBI REA and (right) Chl-a data from ESA CCI L3.




Date : 3 April 2020

Issue : 3.2


The temporal variability is also assessed by comparing mean, median and 80th percentile in model and ocean color data. Averages of monthly values over 1998-2016 are shown in Figure 3, while time series for each month between 1998 and 2016 are shown in Figure 4. The model is able to reproduce the main features of the seasonal cycle: the spring bloom, followed by a decrease of chlorophyll concentration in summer in certain areas, notably the shallow seas, (see fig 4) a second bloom in some areas 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.

The timing of the spring phytoplankton bloom, as expressed by chlorophyll, is fairly consistent with satellite observations (Figure 3). Chlorophyll increases from January to April, with a maximum in April, and then decreases in summer. From the beginning up to the maximum of the spring bloom, the model is more productive than the satellite estimations. The rest of the year, both datasets are very similar in terms of mean and median; the 80th percentile is lower in the model.

Figure 3. Median, mean and 80th percentile for monthly averaged sea surface chlorophyll (mg Chl m-3) from REA (red) and Chl-a data from ESA CCI L3 (blue) (top left, top right and middle left, respectively). Percent of bias, correlation and RMSD (mg Chl m-3) between REA and Chl-a data from ESA CCI L3 (middle right, bottom left and bottom right, respectively).




Date : 3 April 2020

Issue : 3.2


Monthly PB is low in the period June-January, rising to about 40% during the peak of the spring bloom, reflecting more pronounced model bloom. Correlation values dip slightly in January, but care must be exercised here, because of the scarcity of winter data at higher latitudes. In general correlations are reasonable. The Taylor diagram (Figure 6) also points to a larger spread in chlorophyll concentrations in the model between June-October and lower in spring when compared to the observations.

In Figure 4, data was separated into three subdomains: the North West Shelf, comprising the shallow Northern Seas and inner Celtic Sea, the open Atlantic including Biscay, the Mediterranean Sea. Figure 4 shows model and satellite values for 1998-2016 for the whole IBI Service Domain and these 3 subdomains. More comprehensive regional analysis can be found in IV.1.1.

Figure 5 shows variation of correlation and fractional bias between 1998 and 2016. Lower correlations are seen over the Mediterranean and North West Shelf areas.




Date : 3 April 2020

Issue : 3.2


Figure 4. Time series of the mean Chl-a at sea surface over the years 1998-2016 from IBI REA model (red), CMEMS : OCEANCOLOUR_ATL_CHL_L3_NRT_OBSERVATIONS_009_036 (blue) ). All=IBI Service Domain, Atl=Atlantic Ocean (including Biscay), NWS=North West Shelf, Med=Mediterranean. Left: monthly averages, right=annual averages.




Date : 3 April 2020

Issue : 3.2


Figure 5. Fractional bias (percent bias/100) and correlation. Chlorophyll at surface, monthly averages of REA Model and satellite data. All=IBI Service Domain, Atl=Atlantic Ocean (including Biscay), NWS=North West Shelf, Med=Mediterranean.




Date : 3 April 2020

Issue : 3.2


Figure 6. Taylor diagram for annual (‘all’) and monthly (numbers) chlorophyll, 1998-2016 climatology Obs=L3 climatology.




Date : 3 April 2020

Issue : 3.2


IV.1.1 Regional differences: chlorophyll

Figure 7. Regions referred to in text; green lines indicate sections.




Date : 3 April 2020

Issue : 3.2


Variation in surface chlorophyll concentration in the model and satellite data was examined for 14 subregions, shown in Figure 7. Model and satellite data were averaged over 1 month, and also, for time series, over 10 km boxes to improve data coverage for winter months. Results for each subregion are presented in Figure 8 to Figure 14: on the left, time series of monthly averages of chlorophyll concentration over the area are shown for the period 1998-2016, middle and right show annual variations averaged over the same period.

Averages of model concentration for all areas exceed satellite observations. As already said, the model slightly overestimates chlorophyll in coastal productive areas. In the English Channel, a larger area of high productivity is found in the model in the East part of the section (close to the Straits of Dover) during the summer period, with the model showing a second productive area in mid Channel in summer. Spring bloom timing is similar in model and satellite for the Celtic Sea, with the satellite data showing a relatively bigger autumn bloom in the East. Agreement in timing of blooms is good for SW Ireland and NW Ireland, but model levels are lower. To the west of France, model shows higher productivity near the coast (and inside Oleron). Off NW Spain, nearshore model productivity peaks in spring while L3 peaks in autumn; offshore the spring bloom agrees well. Off Portugal, nearshore productivity in the model peaks in August, and in the L3 data, high productivity, at a lower level, lasts from March to October. In the Alboran Sea, model and L3 show high productivity in the W. In the Ligurian-Provencal Sea and Balearics and Algerian Current, there is good agreement in timing of the spring bloom; Off Morocco, the spring bloom offshore agrees well, and the model has a highest autumn bloom inshore. Model levels are higher in the Mediterranean and off Morocco during productive periods.

Figure 15 summarises the regional data. Higher model variability is shown for all regions, especially Western France, Biscay and Portugal; correlations are generally good, particularly for the Balearics and Algerian Current areas, less so off Ireland.




Date : 3 April 2020

Issue : 3.2


Figure 8. Monthly averaged chlorophyll, mg/m3. Left: time series of simulated (green) and satellite (blue) concentrations in sub-regions: English Channel (top) and Irish Sea (bottom), 1998-2016. Right: hovmoller diagrams for REA and L3 at section across each sub-region (see Figure 7), monthly average section data, 1998-2016.




Date : 3 April 2020

Issue : 3.2


Figure 9. Monthly averaged chlorophyll, mg/m3. Left: time series of simulated (green) and satellite (blue) concentrations in sub-regions: Celtic Sea (top) and South West Ireland (bottom), 1998-2016. Right: hovmoller diagrams for REA and L3 at section across e each sub-region (see Figure 7), monthly average section data, 1998-2016.




Date : 3 April 2020

Issue : 3.2


Figure 10. North West Ireland (top) and Western France (bottom), 1998-2016,. Monthly averaged Chlorophyll, mg/m3. Left: area mean: time series of monthly average simulated (green) and satellite (blue) concentrations in sub-regions: Biscay (top) and North West Spain (bottom), 1998-2016. Right: middle: hovmoller diagrams for REA and L3 at section across each sub-region (please refer to Figure 7), monthly average section data, 1998-2016. The gap in the French data is the Ile de Oleron.




Date : 3 April 2020

Issue : 3.2


Figure 11. Monthly averaged Chlorophyll, mg/m3. Left: area mean: time series of monthly average simulated (green) and satellite (blue) concentrations in sub-regions: Biscay (top) and North West Spain (bottom), 1998-2016. Right: middle: hovmoller diagrams for REA and L3 at section across each sub-region (please refer to Figure 7), monthly average section data, 1998-2016.




Date : 3 April 2020

Issue : 3.2


Figure 12. Monthly averaged Chlorophyll, mg/m3. Left: area mean: time series of monthly averagesimulated (green) and satellite (blue) concentrations in sub-regions: Portugal (top) and Alboran Sea (bottom), 1998-2016. Right: middle: hovmoller diagrams for REA and L3 at section across each sub-region (please refer to Figure 7), monthly average section data, 1998-2016.




Date : 3 April 2020

Issue : 3.2


Figure 13. Monthly averaged Chlorophyll, mg/m3. Left: area mean: time series of monthly average simulated (green) and satellite (blue) concentrations in sub-regions: LIgurian Sea (top) and Balearic area (bottom), 1998-2016. Right: middle: hovmoller diagrams for REA and L3 at section across each sub-region (please refer to Figure 7), monthly average section data, 1998-2016.




Date : 3 April 2020

Issue : 3.2


Figure 14. Monthly averaged Chlorophyll, mg/m3. Left: area mean: time series of monthly average simulated (green) and satellite (blue) concentrations in sub-regions: Algerian Current (top) and North West Morocco (bottom), 1998-2016. Right: middle:hovmoller diagrams for REA and L3 at section across each sub-region (please refer to Figure 7), monthly average section data, 1998-2016.




Date : 3 April 2020

Issue : 3.2


Figure 15. Taylor diagram of area averaged chlorophyll concentration for each region, 1998-2015. Satellite L3 and REA.




Date : 3 April 2020

Issue : 3.2


IV.2 Nitrates

Figure 16 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.

There is reasonable agreement, averaged over the basin, and regionally in the water to the west of Ireland and off the shelf to the west of France. The model underpredicts significantly nitrate concentrations in most of the productive areas: the shallow Northern Seas, to the west of Iberia and Morocco, in the Gulf of Cadiz and the Mediterranean, and the shelf to the west of France. This results from the higher chlorophyll concentrations predicted by the model along the coasts.

Time series plots (Figure 17) show that the model underestimates the monthly mean, median and 80th percentile during the first months of the year, which may correspond to the elevated spring phytoplankton bloom in the model (see section IV.1). Correlation coefficient (Figure 18), and PB deteriorate markedly for the time period May-October. Better model performance during winter is also apparent on the Taylor diagram in Figure 18 where all three presented statistics improve significantly for the time period November – April.

Climatological (WOA) and model mean (1993-2016) profiles are shown in Figure 16. Annual average vertical profiles (Figure 19) show good agreement between the model and WOA climatology with some more pronounced mismatch around the water depths 700 – 1000 m. Agreement in the Celtic Sea and North Sea is poor.

Monthly vertical profiles (Figure 19) for the model and WOA2013 are presented for 0-500 m (the limit of monthly WOA fields, or to the bottom if the water column is shallower than 500 m). In shallower waters (Biscay and Celtic Sea), the nitrate concentration at depth is underpredicted, as is the difference between the surface and bottom concentrations. For the location in the Southern North Sea, of depth 30m, the winter concentration in the model is much too low, but the well mixed nature of the water column is captured.

Transects presented in Figure 22 reveal good agreement between the model and WOA2013 climatology.




Date : 3 April 2020

Issue : 3.2


Figure 16. Concentrations of nitrate. (left) IBI REA mean over the years 1993-2016 at sea surface (mmol m-3); (right) Climatology WOA 2013a.




Date : 3 April 2020

Issue : 3.2


Figure 17. Median, mean and 80th percentile for monthly averaged sea surface nitrate (mmol m-3) from REA (red) and WOA13a (blue) (top left, top right and middle left, respectively). Percent of bias, correlation and RMSD (mmol m-3) between REA and WOA13a (middle right, bottom left and bottom right, respectively).




Date : 3 April 2020

Issue : 3.2


Figure 18. Taylor diagram for annual and monthly nitrate for IBI REA (1993– 2016 average) and WOA climatology




Date : 3 April 2020

Issue : 3.2


Figure 19. Vertical profiles on Nitrate, (mmol m-3), red IBI REA, blue WOA13a.




Date : 3 April 2020

Issue : 3.2


Figure 20. Location map for monthly station plots




Date : 3 April 2020

Issue : 3.2


Figure 21. Vertical profile of mean nitrate concentration over the IBI area in mmol m-3 from IBI REA and WOA 2013. Monthly averages, 1993-2016). Top: locations in the Celtic Sea, Biscay,

Iberian Shelf, Moroccan Shelf, North Sea. Bottom: 3 open Atlantic locations and the Mediterranean. Locations given in Figure 20.




Date : 3 April 2020

Issue : 3.2


Figure 22. Mean of nitrate concentration in sea water along various transects in mmol m-3. Left panel: WOA 2013a; right panel: IBI REA. Depths in km.




Date : 3 April 2020

Issue : 3.2


IV.3 Phosphate

Figure 23 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 model concentration is underpredicted in the subtropical gyre, the Mediterranean Sea and off Morocco. During the spring bloom, basin average Phosphate concentration is lower in the model for the mean and 80th percentile, but similar for the median. Poorer model performance in summer manifests in the monthly timeseries by a drop in correlation (see also the Taylor diagram (Figure 29)) and rise in percent bias.

The section plots (Figure 25) from the model and WOA2013 are generally in good agreement, although an underprediction by the model at a depth of about 1000m at 50N can be seen, similarly an over prediction at 30N. Figure 27 and Figure 28 show vertical profile data.

Figure 23. Concentrations of phosphate, mmol/m3. (right) IBI REA mean over the years 1993-2016 at sea surface; (left) Climatology WOA 2013a.




Date : 3 April 2020

Issue : 3.2


Figure 24: Median, mean and 80th percentile for monthly averaged sea surface phosphate (in mmol m-3) from REA (red) and WOA13a (blue) (top left, top right and middle left, respectively), middle right: percent of bias, correlation and RMSD (mmol m-3) between REA and WOA13a (middle right,

bottom left and bottom right, respectively).




Date : 3 April 2020

Issue : 3.2


Figure 25. Mean of phosphate concentration in sea water along various transects in mmol m-3. Right panel: IBI REA; left panel: WOA 2013.




Date : 3 April 2020

Issue : 3.2


Figure 26. Vertical profiles of mean phosphate concentration over the IBI area in mmol m-3 from REA (red) and WOA 2013 (blue).




Date : 3 April 2020

Issue : 3.2


Figure 27. Phosphorous vertical profiles as function of month, WOA and REA (1993-2016). Celtic Sea, Biscay, Iberian Shelf, Moroccan Shelf, North Sea.

Figure 28. Phosphorous vertical profile with month WOA (left and REA (right). Open sea sections (indicated as lon lat), Mediterranean. Locatons given in Figure 20




Date : 3 April 2020

Issue : 3.2


.

Figure 29. Taylor diagram for annual and monthly phosphates for surface IBI REA (1993-2016 average) and WOA 2013a climatology.

IV.4 Silicate

Figure 30 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. Lower surface model concentrations are seen to the South of Ireland and off Morocco, while higher model concentrations exist in the Northwest part of the domain, in Biscay and the Mediterranean.




Date : 3 April 2020

Issue : 3.2


Time series of monthly means (1993-2016; Figure 31) give the median, 80th percentile and mean to be too low in the winter months and too high at other times. Correlation dips to around zero in June, and percent bias peaks. Poor model performance is reflected in the Taylor diagram.

Mean profiles at various stations (Figure 32) give significantly higher concentrations in the North Sea, Celtic Sea and Mediterranean Sea, at all depths, and reduced concentrations in Biscay between 2000 – 4000m.

The section at 18W shows that the high Silicate bottom layer thins less in the climatology than in the model. High silicate water is not seen in the model at the bottom for sections at 55N, but is apparent just to the South of the transect; this water is associated with Antarctic Bottom Water.

Figure 30. Concentrations of silicate (mmol m-3). (right) IBI REA mean over the years 1993-2016 at sea surface; (left) Climatology WOA 2013a.




Date : 3 April 2020

Issue : 3.2


Figure 31. Median, mean and 80th percentile for monthly averaged sea surface silicate (mmol m-3) from REA (red) and WOA13a (blue) (top left, top right and middle left, respectively). Percent of bias, correlation and RMSD (mmol m-3) between REA and WOA13a (middle right, bottom left and bottom

right, respectively).




Date : 3 April 2020

Issue : 3.2


Figure 32. Vertical profiles of Silicate, (mmol m-3), red IBI REA, blue WOA13a.




Date : 3 April 2020

Issue : 3.2


Figure 33. Silicate concentration in sea water along various transects in mmol m-3. Right panel: IBI REA; left panel: WOA 2013.




Date : 3 April 2020

Issue : 3.2


Figure 34. Taylor diagram for annual and monthly silicates for IBI REA (1993-2016 average) and WOA 2013a climatology.




Date : 3 April 2020

Issue : 3.2


IV.5 Oxygen

Figure 35 shows 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 constrains strongly the solubility of atmospheric oxygen at sea surface.

The annual pattern of mean surface O2 is well represented (Figure 36). Correlation is good at all months, and there is good agreement in the median, mean and 80th percentile. Percent bias values are very low. Profiles in the Celtic Sea and North Sea show a more oxygenated water column.

Figure 35. Concentrations of dissolved oxygen). Right: IBI REA, mean over the years 1993-2016 at sea surface (mmol m-3). Left Climatology: WOA 2013a.




Date : 3 April 2020

Issue : 3.2


Figure 36. Median, mean and 80th percentile for monthly averaged sea surface oxygen (mmol m-3) from REA (red) and WOA13a (blue) (top left, top right and middle left, respectively). Percent of

bias, correlation and RMSD (mmol m-3) between REA and WOA13a (middle right, bottom left and bottom right, respectively).




Date : 3 April 2020

Issue : 3.2


Figure 37. Taylor diagram for annual and monthly oxygen at sea surface for iBI REA 1993 – 2016 average) and WOA 2013 climatology Monthly averaged sea surface.




Date : 3 April 2020

Issue : 3.2


Figure 38. Mean of oxygen concentration in sea water along various transects in mmol m-3. Left panel: IBI REA; right panel: WOA 2013




Date : 3 April 2020

Issue : 3.2


Figure 39. Vertical profiles of Oxygen, (mmol m-3), red IBI REA, blue WOA13a.




Date : 3 April 2020

Issue : 3.2


Figure 40. Oxygen profiles as function of month, WOA and REA (1993-2016). Top: locations in various basins, Bottom: open sea sections (indicated as lon lat), Mediterranean. Locatons given

in Figure 20




Date : 3 April 2020

Issue : 3.2


IV.6 Primary productivity

Total primary productivity in the IBI REA model is compared to the VGPM model, which derives productivity values from satellite measurements of ocean colour:

(http://www.science.oregonstate.edu/ocean.productivity/standard.product.php).

Mean spatial distribution of depth-integrated primary productivity is presented on Figure 41. Statistics are presented on Figure 42 to Figure 46. Monthly average timeseries (2002-2016) are shown in Figure 43. 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. The Taylor plot of the results (Figure 46) confirms this. 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). Also, 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”. Figure 41 shows that spatial distribution of primary productivity in the model and VGPM are similar. Regional differences are still present: the model has more production in April-June in the Southern part of the domain as well as in Mediterranean Sea. In October, simulated primary production is clearly underestimated in the whole domain.

http://www.science.oregonstate.edu/ocean.productivity/standard.product.php




Date : 3 April 2020

Issue : 3.2


Figure 41. Maps of depth-integrated primary productivity during spring (Apr-Jun; bottom) and October (top). Mean for the VGPM model (left; nammed OSU of the figure) and REA model (right)

over the years 2002-2016. Values are normalised relative to the basin mean for each period; means (mgC m-2 day-1) given in the title fo each picture.




Date : 3 April 2020

Issue : 3.2


Figure 42. Time series of correlation coefficient and percentage of bias for primary productivity. Monthly, 2002-2016.




Date : 3 April 2020

Issue : 3.2


Figure 43. Median, mean and 80th percentile for monthly averaged total primary productivity (mgC m-2 day-1) from REA (red) and and Satellite derived VGPM estimation (blue) (top left, top right and

middle left, respectively). Percent of bias, correlation and RMSD (mgC m-2 day-1) between REA and and Satellite derived VGPM estimation (middle right, bottom left and bottom right,

respectively).

Figure 44 shows the timeseries of the two signals over a period of 14 years. Some interannual variability is apparent in the VGPM data, but this is not fully reflected in the model. Regarding the phasing of the productivity signal, it is apparent from Figure 43, Figure 44, and Figure 45, that the peak in primary productivity is earlier in the model than in the VGPM data when averaged over the whole IBI Service Domain. Lagged correlation analysis of monthly timeseries gives a lead of about 40 days.




Date : 3 April 2020

Issue : 3.2


Figure 44. Monthly averaged total primary productivity (mgC m-2day-1). REA (green) and VGPM (blue).

Figure 45. Correlation between VGPM and IBI REA: left: correlation as function of lag: a

peak in the correlation for positive lag indicates that the REA model leads the

VGPM model.




Date : 3 April 2020

Issue : 3.2


Figure 46: Taylor diagram for annual and monthly primary productivity for IBI REA (2002-2016 average) and VGPM climatology.




Date : 3 April 2020

Issue : 3.2


IV.7 Euphotic layer depth

Model results are compared with Globcolour data, which are calculated from satellite observations of ocean colour. Satellite observations are sparse in winter in Northern latitudes, so at this period, statistics will be more representative of the conditions in the south of the model domain. The euphotic layer depth is mainly well represented in the model (Figure 47). Some discrepancies exist in the north-west part of the domain, in the sub-tropical gyre and Irish Sea, where the model overestimates its depth and in the Alboran Sea, Gulf of Lions and Biscay, where the model is slightly high. 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 coastal 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 48). The timing of these minima shows the model to be well phased. The maximum occurs later (October-November) in the model than in the observations (August-September). The Globcolour dataset shows a second peak in Feb (most apparent in the median data) that is not apparent in the model data; this double peak is also apparent in the full timeseries of mean data (Figure 50). As regards the amplitudes, the maximum depth (in winter) tends to be over-predicted by the model, whereas the minima agree well. The correlation coefficient (Figure 49) drops to 0.7 in January and exceeds 0.8 the rest of the year. This is confirmed by the values of PB, which exceeds 20% from October to January. The Taylor diagram (Figure 50), also suggests that the December-February months are characterised by the poorest skill, whereas the remaining months are comparable. Inter annual variations in the Globcolour data (Figure 50) are not reflected in the model values.




Date : 3 April 2020

Issue : 3.2


Figure 47. Euphotic layer depth (m). Mean over the years 1998-2016; (left) IBI REA; (right) Globcolour.




Date : 3 April 2020

Issue : 3.2


Figure 48. Median, mean and 80th percentile for monthly averaged Euphotic depth (m) from REA (red) and GlobColour data (blue) (top left, top right and middle left, respectively). Percent of bias,

correlation and RMSD (m) between REA and GlobColour data (middle right, bottom left and bottom right, respectively).




Date : 3 April 2020

Issue : 3.2


Figure 49. Time series of correlation coefficient and percentage of bias for euphotic layer depth.




Date : 3 April 2020

Issue : 3.2


Figure 50: (top) Time series of mean euphotic layer depth for IBI REA and Globcolour. (bottom left:Scatter plot of monthly mean basin averages. Bottom right: timeseries of annual averages.




Date : 3 April 2020

Issue : 3.2


Figure 51.Taylor diagram for annual and monthly euphotic layer depths for IBI REA (1998-2016 average) and Globcolour climatology.




Date : 3 April 2020

Issue : 3.2


V IN SITU DATA

V.1 IBI comparison with in-situ data from Bio-Argo

Bio Argo data (chlorophyll and nitrate) is limited in the Atlantic to one float around 18W and 50N; as it can be seen in Figure 52 there is better coverage in the Atlantic for O2.

Figure 52. Availability of Argo float data.

Model data, from IBI V4 daily model files, corresponding to each Argo data point was collocated (nearest neighbour in the horizontal, interpolated in the vertical). Some comparisons are shown hereafter for chlorophyll (Figure 53), nitrate (Figure 54) and dissolved oxygen (Figure 55).




Date : 3 April 2020

Issue : 3.2


Figure 53. Chlorophyll, mg/m3. Bio Argo (top) and REA model output (middle), Argo float trajectory (bottom). REA data gaps cover times when the Argo float was out of the REA service area.




Date : 3 April 2020

Issue : 3.2


Figure 53 shows chlorophyll profiles to the West of Ireland for more than 2 years in 2014-2016. Deepening of the mixed layer is apparent in winter Argo and model results. The spring bloom is apparent during spring in 2015 and 2016, followed by a deepening of the maximum chlorophyll in summer, slight deepening in Argo while the model simulates a deeper and longer deep chlorophyll maximum.

Nitrate data is shown in Figure 54 for the same Argo deployment (float number 5904479) in the Atlantic. There is no Quality Control check for nitrate at the moment, bias and instrument saturation are possible, but vertical gradient can be analysed. Simulated concentrations are lower than for the Argo data, but here, the vertical variation of nitrate is similar enough (correlation coefficient=0.9). Late summer surface depletion, reflecting biological uptake, is apparent for both model and Argo data.




Date : 3 April 2020

Issue : 3.2


Figure 54. Nitrate, (mmol m-3). Bio Argo (top), REA model output (middle), Argo float trajectory (bottom). REA data gaps cover times when the Argo float was out of the REA service area.




Date : 3 April 2020

Issue : 3.2


A comparison of Argo and model oxygen in the Atlantic is shown in Figure 55 for the same float. Model oxygen is higher than the Argo measurements by about 20 mmol/m3; the depth of the oxygen minimum is similar, but the oxygen minimum in the model is less pronounced. The depth of the oxygen minimum is somewhat less for Argo than for the model (between 500 and 1000m depth). Winter deepening of the oxygen rich surface layer is similar.

Figure 56 and Figure 57 summarise the Argo datasets. The depth of the oxygen minimum or chlorophyll maximum was extracted for each Argo profile, where possible. For chlorophyll, the bias for individual profiles is positive for the Atlantic (model chlorophyll is higher than Argo data for the profile) and negative for the Mediterranean. Where clear oxygen minima are present, they tend to be deeper in the model, as is the case with the depth of chlorophyll maxima.




Date : 3 April 2020

Issue : 3.2


Figure 55. Oxygen, mmol m-3. Bio Argo (top), REA model output (middle), Argo float trajectory (bottom).




Date : 3 April 2020

Issue : 3.2


Figure 56. Chlorophyll. Argo data and interpolated REA output. From left to right: percent bias of individual profiles (model-Argo), normalised RMSD of individual profiles, and difference in depth of

Chlorophyll maximum (model-argo).




Date : 3 April 2020

Issue : 3.2


Figure 57. Oxygen, mmol/m3. Argo data and interpolated REA output. From left to right: percent bias of individual profiles (model-Argo), normalised RMSD of individual profiles, difference in

depth of oxygen minimum, (model-argo).

V.2 Cruise data

Data was assembled through the EMODNET site. For files that were not in netcdf format, Ocean Data View (ODV, https://odv.awi.de) was used to extract quality-controlled data to netcdf format. Numerous datasets were available over the IBI REA period. A large proportion of the data was collected with stations occupied in sections (line) or boxes during a single cruise over a time period of the order of one week. Coverage varies with parameter; see Figure 58 and Figure 59.




Date : 3 April 2020

Issue : 3.2


Figure 58. chlorophyll data availability.

Figure 59. Silicate data availability. Selected sections are in red.

In situ data for individual sections (or boxes) was compared to the nearest REA model field. Examples are shown in Figure 60 and Figure 61. In these cases, good agreement between model and in situ measurements can be seen in the regression lots and sections.




Date : 3 April 2020

Issue : 3.2


Figure 60. Chlorophyll section, 1996. Top left: station positions. Bottom left: regression plot of all section data. Top right: in situ chlorophyll concentrations, mg/m3. Bottom right:

interpolated REA results.




Date : 3 April 2020

Issue : 3.2


Figure 61 Phosphorous section, 2001. Top left: station positions. Bottom left: regression plot of all section data. Top right: in situ phosphorous concentrations, mmol/m3. Bottom right: interpolated

REA results.

Individual station data is shown in Figure 62 to Figure 71. The scatter plot of nitrate data (all depths and seasons, Figure 62) shows generally good agreement (slope 0.82, correlation coefficient0.92) over the dataset. Outliers (chosen by eye and shown in both the location map and scatter plot) are concentrated off the mouth of the Rhine, and show higher in situ data compared with the model. % bias for the deeper stations (Figure 63) is generally good (<10%), as is normalised RMSD, with the exception of a line off Morocco, close to the Southern boundary of the model. Correlation is good.

The scatter plot of phosphorous is shown in Figure 64. Again, agreement is generally good, (slope of the regression fit 0.85); the correlation 0.67 is lower than for nitrate, with outliers to be found off the mouth of the Rhine, as well as in the Wash, off the east coast of the UK. Figure 65 shows the percent bias is generally below 40%, with correlation and RMSD generally better offshore.

Figure 66 shows the scatter plot for silicate. The regression slope (.85 and correlation (0.92) are good over the dataset. Outliers are seen off the Loire, and in the deeper stations. Figure 67 shows that the % bias is variable, but, like the RMSD, better offshore. Correlation is good, but less so in Biscay.




Date : 3 April 2020

Issue : 3.2


Figure 62. Nitrate (mmol/m3) from in situ measurements and REA model. Left: Station locations. Right scatter plot (a=slope, b=intercept, c=correlation coefficient. Outlying point, chosen by eye,

are in red in both panels.




Date : 3 April 2020

Issue : 3.2


Figure 63. Nitrate, deeper stations. Left percent bias for individual station data. Middle normalised root mean square difference, mmol/m3. Right. Correlation for individual station data.




Date : 3 April 2020

Issue : 3.2


Figure 64. Phosphate (mmol/m3) from in situ measurements and REA model. Left: Station locations. Right scatter plot (a=slope, b=intercept, c=correlation coefficient. Outlying point, chosen

by eye, are in red in both panels




Date : 3 April 2020

Issue : 3.2


Figure 65. Phosphate, deeper stations. Left percent bias for individual station data. Middle normalised root mean square difference, mmol/m3. Right. Correlation for individual station data.




Date : 3 April 2020

Issue : 3.2


Figure 66. Silicate (mmol/m3) from in situ measurements and REA model. Left: Station locations. Right scatter plot (a=slope, b=intercept, c=correlation coefficient. Outlying point, chosen by eye,

are in red in both panels




Date : 3 April 2020

Issue : 3.2


Figure 67. Silicate, deeper stations. Left percent bias for individual station data. Middle normalised root mean square difference mmol/m3. Right. Correlation for individual station data.




Date : 3 April 2020

Issue : 3.2


Figure 68. Example Chlorophyll profiles from 9 stations: blue is in situ data, red: interpolated REA model results. Depths (y axis) are in km. Chlorophyll concentrations in mg/m3




Date : 3 April 2020

Issue : 3.2


Figure 69. Example oxygen profiles from 9 stations: blue is in situ data, red: interpolated REA model results. Depths (y-axis) are in km. Oxygen concentrations in mmol/m3

A small subsample of individual station data for Chlorophyll, and Oxygen is shown in Figure 68 and Figure 69. For the subsample, most of the in situ chlorophyll profiles show sub surface maxima, and this is usually, but not always reflected in the model output. Similarly, oxygen minima are commonly observed in the oxygen profiles of both the in situ data and the model data in waters of >500m depth.

Figure 70 shows a comparison of oxygen data for deeper stations. % bias for individual profiles is low (generally <10%, except for stations to the N of Spain, and normalised RMSD is usually within 25 mmol/m3. Correlation for individual station data and model output is generally high, indication a good profile shape). The depth of the oxygen minimum is generally overestimated in the model, with the exception of the shelf edge to the W of Northern France and the Celtic Sea.

Figure 71 summarises chlorophyll profile data. Higher % bias and RMSD are apparent in coastal areas. Correlation is generally good, apart from stations in the Mediterranean and off the Bristol Channel. The depth of the chlorophyll maximum is generally good in the model, with the exception of some stations over the shelf edge off the Celtic Sea.




Date : 3 April 2020

Issue : 3.2


Figure 70. Oxygen for deeper stations. Left percent bias for individual station data. Middle left normalised root mean square difference, mmol/m3. Middle right. Correlation for individual station

data. Right difference in depth (model-in situ, m) of oxygen minimum.




Date : 3 April 2020

Issue : 3.2


Figure 71. Chlorophyll for deeper stations, in situ station data and REA output (interpolated). Left percent bias for individual station data. Middle left normalised root mean square difference,

mg/m3. Middle right: correlation for individual station data. Right: difference in depth (model-in situ, m) of chlorophyll maximum.

V.3 Ferrybox data

The Pride of Bilbao was equipped with sensors to measure fluorescence, temperature and salinity from 2003-2010. Cruise paths for 2007 are shown in Figure 72: approximately weekly transects of surface flurorescence in the Western English Channel and across Biscay. Direct Chl-a fluorescence data are difficult to interpret due to the dependence of the fluorescence on the pre-illumination, species and photophysiology of the plankton; here chlorophyll concentration is assumed to be directly proportional to fluorescence.

Results from the ferrybox and REA model for 2007 are compared in Figure 73. The cruise path was divided into sections covering Biscay (latitude <48.4N, i.e to the W of Ouessant) and English Channel. In general, model chlorophyll for the Channel is higher throughout the year above 50.4N, i.e. to the South of the Isle of Wight. North of Brittany (48.5-49.5N), the ferrybox indicates high productivity before day 200, with the model showing a smaller increase later on.




Date : 3 April 2020

Issue : 3.2


For the Biscay section, both model and ferrybox show high productivity between days 50 and 200, continuing into the autumn above 47N; high productivity near the Spanish coast is more persistent in the ferrybox results.

Figure 72. Cruise path, Pride of Bilbao, 2007.




Date : 3 April 2020

Issue : 3.2


Figure 73. Left: REA model surface chlorophyll (mg/m3), and right: scaled Ferrybox fluorescence, 2007. Top panels: northern section (Western English Channel); bottom: Biscay. X axis is Julian Day.




Date : 3 April 2020

Issue : 3.2


VI QUALITY CHANGES SINCE PREVIOUS VERSION

The new system and the previous one are noticeably different. The physical and biogeochemical models were updated. The physics from NEMO2.3 was used in the previous system while it comes from NEMO3.6 in the new one. The biogeochemical model PISCES in its version 3.2 was used in the previous system while PISCES3.6 is now used. Initial and open boundary conditions were also revised. For more details, please refer to the PUM documents.

Here, we present the added value of the new system to the simulated ecosystem. Main improvements are observed on the mean distribution and seasonal amplitude of sea surface chlorophyll (Section VI.1). However, identification of which modification has improved the biogeochemical results is excluded because of the whole system update.

Following the CMEMS recommendations, the carbon cycle was also improved. None product concerning the carbon cycle is delivered, but two variables are produced and stored, by now, only for internal uses. We expect to deliver them soon. These variables are pH and surface ocean partial pressure of CO2 (named spCO2). A first evaluation of spCO2 is given in Section VI.2.

VI.1 Chlorophyll

The new system improves the annual mean sea surface chlorophyll distribution (Figure 74). Concentrations are now lower in the Atlantic part of the domain and in North Sea. As well, open boundaries in the vicinity of the subtropical gyre are more realistic. Bias between model and ocean color product is improved in the Atlantic, North Sea, and Mediterranean Sea (Figure 75). But the new simulation loses high chlorophyll in the Norway current and over-predicts chlorophyll in the productive coastal areas to the West of France and Southern North Sea due to higher input of nutrients from river discharges as compared to the previous simulation.

Also, mean seasonal amplitude of sea surface chlorophyll is more realistic (Figure 76). In the previous system, amplitude was too high in the whole domain, especially north of 35°N, in the Atlantic and Mediterranean as compared to ocean color product. The new system allows reducing this seasonal amplitude and getting it closer to the satellite estimation.




Date : 3 April 2020

Issue : 3.2


Figure 74. Annual mean sea surface chlorophyll (mg Chl m-3) for years 2002 to 2014. Left) Ocean colour product from ESA-CCI, middle) previous version and, right) new reanalysis.

Figure 75. Bias of log transformed sea surface chlorophyll for years 2002 to 2014. Log(IBI model) – log(ocean colour product). Left) previous version and, right) new reanalysis.




Date : 3 April 2020

Issue : 3.2


Figure 76. Mean seasonal amplitude of sea surface chlorophyll (mg Chl m-3) for years 2002 to 2014. Left) Ocean colour product from ESA-CCI, middle) previous version and, right) new reanalysis.

VI.2 Carbon system

In previous version, no variable concerning the carbon cycle was delivered in CMEMS. Atmospheric partial pressure of CO2 was taken as a constant value, meaning that no anthropic effect was considered. The condition at the surface boundary of the CO2 partial pressure now takes into account the anthropogenic effects. The system reads a monthly mean carbon dioxide (expressed as a mole fraction in dry air, µmol/mol, abbreviated as ppm) globally averaged over marine surface sites (Conway et al., 1994; Masarie and Tans, 1995; https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html) and the total atmospheric pressure at 3 hours from ERA-Interim, the same as used for the physics in order to be coherent.

A first evaluation of spCO2 is given here by comparing simulated spCO2 to the updated observation-based global monthly gridded spCO2 product from 1982 through 2015 of Landshutzer et al. (2017).

Hovmöller diagrams (Figure 77) and time series (Figure 78) show the spCO2 at 15°W. In the South part of the domain (subtropical gyre), seasonal variations of spCO2 show an alternation of entrance of CO2 to the ocean (ingassing) and outgassing of CO2 to the atmosphere. SpCO2 is minimum during winter (February-March) and lower than atmospheric CO2, so the ocean acts as a carbon sink. To the contrary, spCO2 is maximum in summer (August-September), and it is higher than atmospheric CO2, then generating an outgassing of CO2 to the atmosphere. This alternation of ocean carbon sink and outgassing in the south part of the domain reaches 45°N in the data while it goes up to 50-55°N in the model (not shown). As a result, in the south part of the domain, the simulation is close to the data; both time series are in phase, even if the model simulates slightly higher spCO2 values during summer.




Date : 3 April 2020

Issue : 3.2


In the north part of the domain, oceanic pCO2 remains lower than atmospheric pCO2, so this area acts as an ocean carbon sink all year long, with an increased ingassing CO2 flux during summer and a weaken flux during winter. However, Figure 77 and Figure 78 show that both time series of oceanic spCO2 are not correlated. In fact, minima in summer and maxima in winter are in phase but the first small peak in the data (in late summer) is too high in the model, it is of the same order as the second peak in winter. This is due to the transition area, between the southern seasonally outgassing area and the northern permanently ingassing area, that is northward shifted in the model, as described above.

Figure 77. Surface ocean pCO2 (µatm). Hovmöller diagrams at 15°W. Time series from Landshutzer et al. (2017) (top) and model (bottom).




Date : 3 April 2020

Issue : 3.2


Figure 78: Time series of surface pCO2 (µatm) averaged between 55-60°N (top) and between 32-37°N (bottom).




Date : 3 April 2020

Issue : 3.2


VII TEMPORAL EXTENSIONS

The CMEMS IBI REA system has been extended to cover the period 01/1992 to 12/2018. As a reminder, Table 4 describes the historical evolution of the product.

Table 4: Historical evolution of the IBI Multi-Year Biogeochemical Hindcast System along the Service. Time coverage of each model set-up used to produce the IBI REA product

(IBI_REANALYSIS_BIO_005_003), as well as main novelties introduced.

System Version

(Project/Service)

Operational launch

Novelties

IBI-V2

(MyOcean2)

19/04/2016 First release of the IBI BIO REA hindcast. The IBI biogeochemical Product, delivered to users from CMEMS V3 release, (temporal coverage 2002-2014).

IBI-V4

(CMEMS-I)

26/04/2018 New CMEMS V4 IBI REA System used to run the period 1992-2016. Both physical and biogeochemical models updated (NEMO/PISCES V3.6). In the BIO model, the carbon cycle was improved (The condition at the surface boundary of the CO2 partial pressure now takes into account anthropogenic effects). The physical solution where coupled improved with a new Data assimilation scheme and new observational data sources included.

TEMPORAL EXTENSION

(RFC CCIBI-107)

(CMEMS-II)

16/04/2019 Extension of the CMEMS IBI REA system to cover the period 01/01/1992 to 24/12/2017.

In addition to the year 2017, the years 2014 to 2016 were run again to take into account new reprocessed observational and forcing data for the physical model.

TEMPORAL EXTENSION

Extension of the CMEMS IBI REA system to cover the period 01/01/1992 to 24/12/2018.

In addition to the year 2018, the years 2014 to 2017 were run again to take into account new reprocessed observational and forcing data for the physical model. In particular, the atmospheric forcing data is now ECMWF ERA5, as opposed to the previous ECMWF ERA Interim forcing data. See Section VII.2.1 for more information.




Date : 3 April 2020

Issue : 3.2


VII.1 Extension to the year 2017

There is no change in the biogeochemical model. But, in order to integrate the reprocessed physical data in the assimilation system of the IBI REA PHY and to be consistent with the parent physical model (GLORYS2V4) used to the open boundaries, the extension of the year 2017 started in 2014. Consequently the years 2014 to 2017 has been performed.

We first evaluate the annual mean sea surface chlorophyll-a concentrations (Chl-a) for year 2017 (Figure 79). On annual average, the large-scale distribution of Chl-a is correctly reproduced: the North Atlantic subtropical gyre with low concentrations (< 0.1 mg Chl m-3), increasing concentrations when moving to the north, and the highest values on the continental shelf. The model simulates a higher annual average in the northern part (southern North Sea, English Channel, Irish Sea), the French coast of the Bay of Biscay and the Alboran Sea.

The seasonal cycle of Chl-a averaged over the IBI Service Domain (Figure 80) is in phase with satellite estimates, with a correlation of 0.8. After a winter minimum, a Chl-a peak develops in spring, followed by a summer decrease in biomass, and a second and smaller peak can be observed in autumn.

Figure 81 presents the time series of Chl-a in 8 small boxes. In general, the model predicts the seasonal cycle of Chl-a quite well. Coastal ecosystems of the Bay of Biscay (box 3), the upwelling off Portugal and Morocco (boxes 4 and 7), the Gulf of Cadiz and the Alboran Sea (boxes 5 and 6) succeed in reproducing the seasonal cycle of Chl-a, with a high correlation coefficient between the model and the data. In the English Channel (box 1), high simulated Chl-a persists in summer while remote sensing data predict a sharp decrease after the spring bloom.




Date : 3 April 2020

Issue : 3.2


Figure 79: Annual average of sea surface Chl-a for year 2017 expressed in mg Chl m-3, a) IBI REA and b) ESA CCI ocean colour product. The model is masked as a function of the data.

Figure 80: Time series of Sea surface Chl-a (mg Chl m-3) between 1998 and 2017. IBI-REA-BIO is in black and ESA-CCI ocean colour product in red with associated error in grey. The model is masked as a function of the data. The correlation between the model and the data is indicated in the top-

left.




Date : 3 April 2020

Issue : 3.2


Figure 81: Time series of Sea surface Chl-a (mg Chl m-3) between 1998 and 2017. IBI-REA-BIO is in black and ESA-CCI ocean colour product in red with associated error in grey. Concentrations are averaged over 8 small boxes as defined in the map on the top-right. The model is masked as a

function of the data. The correlation between the model and the data is indicated in the top-left of each panel (continues in next pages).




Date : 3 April 2020

Issue : 3.2


Figure 81: (continues) Time series of Sea surface Chl-a (mg Chl m-3) between 1998 and 2017. IBI-REA-BIO is in black and ESA-CCI ocean colour product in red with associated error in grey.

Concentrations are averaged over 8 small boxes as defined in the map on the top-right. The model is masked as a function of the data. The correlation between the model and the data is indicated in

the top-left of each panel (continues in next pages).




Date : 3 April 2020

Issue : 3.2


Figure 81: (continues) Time series of Sea surface Chl-a (mg Chl m-3) between 1998 and 2017. IBI-REA-BIO is in black and ESA-CCI ocean colour product in red with associated error in grey.

Concentrations are averaged over 8 small boxes as defined in the map on the top-right. The model is masked as a function of the data. The correlation between the model and the data is indicated in

the top-left of each panel.




Date : 3 April 2020

Issue : 3.2


VII.2 Extension to the year 2018

Similarly, to Section VII.1, the extension to encompass the year 2018 was ran from 2014 onwards.

The IBI-REA-BIO annual mean surface chlorophyll-a (Chl-a) concentrations (Figure 82a) agree largely with the annual average L3 Chl-a concentrations (Figure 82b). Observations from the 2017-year extension persist in 2018, with the low concentrations in the North Atlantic subtropical gyre reproduced. The order of magnitude of Chl-a concentrations are consistent between the satellite observations and the model. Notable differences between satellite and model Chl-a exist in the Alboran Sea, in the waters off North-West Ireland, and in the nearshore coastal waters. The annual seasonal cycle from the satellite and model are in phase throughout the IBI Service Domain, with a correlation of 0.85 (Figure 83).

In eight subsections of the IBI region (Figure 84), the seasonal cycle of Chl-a is in phase with satellite observations. Correlation between the model and satellite observations vary spatially, with two distinct subgroups. The highest correlation is observed in the Biscay area (Box 3), the Alboran sea (Box 5) and north east Macaronesia (Box 8). The most notable difference between model and satellite, are the English Channel (Box 1), the north east Atlantic (Box 2) and the Gulf of Cadiz (Box 6).

The average model nitrate concentrations from 1992 through to the end of the 2018 extension (Figure 85a) generally agree with the reference climatological nitrate maps of the World Ocean Atlas 2013 (Figure 85b), although a number of differences exist. The model under-predicts nitrate concentrations in the Irish Sea, English Channel and southern North Sea, and productive shelf seas in general.

Figure 82: Annual-average sea surface Chl-a (mg Chl-a m-3), for the year 2018. (a) IBI-REA-BIO, and (b) ESA-CCI L3 satellite product.




Date : 3 April 2020

Issue : 3.2


Figure 83: Spatially averaged monthly timeseries of Chl-a (mg Chl-a m-3) in the IBI region from 1998 to 2018. The black timeseries is the IBI-REA-BIO model, while the red timeseries is the ESA-CCI L3

satellite Chl-a product. The grey area bounding the red line is the timeseries of satellite Chl-a error. The correlation between the two Chl-a timeseries is indicated in the top left corner.




Date : 3 April 2020

Issue : 3.2


Figure 84: Spatially averaged monthly timeseries of Chl-a (mg Chl-a m-3) in eight boxes within the IBI region from 1998 to 2018. The eight boxes are indicated in the map on the right hand side. The black timeseries in each subplot is the IBI-REA-BIO model, while the red timeseries is the ESA-CCI L3 satellite Chl-a product. The grey area bounding the red line is the timeseries of satellite Chl-a

error. The correlation between the two Chl-a timeseries is indicated in the top left corner.




Date : 3 April 2020

Issue : 3.2


Figure 85: Nitrate concentrations in the IBI region (a) IBI-REA-BIO average from 1992 to 2018 (b) World Ocean Atlas 2013, v2.

VII.2.1 ERA atmospheric forcing

The IBI MFC completed an assessment of impacts on the IBI multiyear products associated with changing the atmospheric forcing provided by ECMWF from ERA Interim to ERA5. The IBI MFC completed this exercise in anticipation of operational adoption of ERA5 atmospheric forcing for the 2018 extension onwards. Due to the observed influence of ERA5 atmospheric forcing on Sea Surface Temperature, the assessment also encompassed the IBI BGC multiyear products.

In the context of the IBI biochemical non-assimilative hindcast service, the assessment entailed three datasets pertaining to the year 2017: 1) The 2017 monthly mean Chl-a concentrations from the outgoing IBIRYS BIO model forced by ERA-Int, hereafter referred to as ERAi Chl-a, 2) The equivalent 2017 monthly mean Chl-a concentrations from the IBIRYS BIO model forced by ERA5, hereafter referred to as ERA5 Chl-a, and 3) The ESA-CCI L4 monthly Chl-a concentrations at 1 km resolution for 2017, referred to as satellite Chl-a.

Due to the higher relative resolution of satellite chlorophyll, satellite Chl-a was spatially averaged to the native IBIRYS model grid to facilitate statistical comparison. Satellite coverage varied seasonally. Consequently, comparison on an annual basis was restricted to grid points for which satellite data coverage was consistent.

From Figure 86, the difference in ERA forcing does not qualitatively alter the spatial gradient of Chl-a concentrations. Under ERA5 forcing, winter Chl-a concentrations are higher in the oligotrophic waters




Date : 3 April 2020

Issue : 3.2


south of 40°N, and in particular off southwest Morocco. ERA5 forcing also results in lower summer Chl-a and higher spring Chl-a in the Ligurian Provencal Sea.

Satellite coverage seasonally restricts spatial comparison to locations with uninterrupted satellite coverage. Viewing percentage bias in Figure 87, the zone of Chl-a underestimation off the Moroccan coast is more confined in winter under ERA5 forcing. During spring, overestimation is apparently greater in the Alboran Sea and coastal northwest Iberia, while the extent of overestimation is reduced in the Gulf of Cadiz. The influence of ERA5 forcing in summer is confinement of overestimation in Moroccan coastal waters, although overestimation is greater in extent and magnitude to the west of the Iberian Peninsula. Autumnal Chl-a concentrations in the northeast Atlantic are broadly underestimated by the IBIRYS system irrespective of the ERA forcing. ERA5 forcing leads to greater overestimation of autumnal Chl-a concentrations in the Alboran sea.

Considering correlation based on annually aggregated datasets, regional improvement in the correlation between ERA5 chlorophyll and the satellite Chl-a is apparent between the Moroccan coast and the Canary Islands (Figure 88).

In summary, adopting ERA5 atmospheric forcing data instead of ERA-Interim had no net impact on Chl-a concentrations in temperate waters in the sensitivity test of IBIRYS. There was an improvement in the accuracy of Chl-a simulation in the subtropical northeast Atlantic waters adjacent to the Canary Islands and Moroccan coast.




Date : 3 April 2020

Issue : 3.2


Figure 86: ERAi seasonal mean chlorophyll concentrations (top) and ERA5 seasonal mean chlorophyll concentrations (bottom). The seasons are as follows, (left to right): winter – January to March, spring – April to June, summer – July to September, autumn – October to December.




Date : 3 April 2020

Issue : 3.2


Figure 87: Change in percentage bias of IBIRYS seasonal chlorophyll concentrations with ERA5 atmospheric forcing. Values are determined for each season by subtracting ERAi bias (top row, Figure 86) from ERA5 bias (bottom row, Figure 86); positive (red) indicates an increase in bias,

while a negative value (blue) indicates reduced bias.




Date : 3 April 2020

Issue : 3.2


Figure 88: The spatial difference in correlation terms between ERA5 and ERAi chlorophyll.

VII.3 New Interim production

Since July 2020, the IBI BIO reanalysis is updated following a new catalogue policy destined to ensure the dissemination of a quasi-near real time reanalysis timeseries.

For this purpose, every month (M), interim MY products covering the previous month (M-1) are generated with the same model and configuration used for the current reanalysis. For generating the M-1 outputs, the PISCES model is coupled to the ocean circulation model, which is forced with ERA5T atmospheric data from the ECMWF and assimilates Near Real Time (NRT) upstream data due to the lack of available Reprocessed (REP) data for such a short term.

Then, at a biannual frequency, the model is rerun from M-18 to M-13 months and coupled to the ocean circulation model, which uses REP upstream data and ERA5 ECMWF atmospheric forcing, in order to ensure that the best available MY products are generated.




Date : 3 April 2020

Issue : 3.2


VIII REFERENCES

Aumont, O. and Bopp, L. 2006: Globalizing results from ocean in situ iron fertilization studies. Global Biogeochem. Cycles. 20 (2):10–1029.

Bopp L., Aumont, O., Cadule, P., Alvain, S. and Gehlen, M. 2005: Response of diatoms distribution to global warming andpotential implications: A global model study. Geophys. Res. Lett.. 32, L19606, doi:10.1029/2005GL023653.

Conway, T. J., P.P. Tans, L.S. Waterman, K.W. Thoning, D.R. Kitzis, K.A. Masarie, and N. Zhang, (1994), Evidence of interannual variability of the carbon cycle from the NOAA/CMDL global air sampling network, J. Geophys. Research, vol. 99, 22831-22855.

Campbell et al. 2002: Comparison of algorithms for estimating ocean primary production from surface chlorophyll, temperature, and irradiance, Global Biogeochem. Cycles, 16 (3), doi: 10.1029/2001GB001444.

Conkright, M.E., Locarnini, R.A., Garcia, H.E., O’Brien, T.D., Boyer, T.P., Stephens, C. and Antonov, J.I. 2002: WorldOcean Atlas2001: Objective Analyses, Data Statistics, and Figures, CD-ROM Documentation. National Oceanographic DataCenter, SilverSpring, MD, 17 pp.

Garcia, H. E., Locarnini, R.A., Boyer, T.P., Antonov, J.I., Baranova, O.K., Zweng, M.M., Reagan, J.R. and Johnson, D.R., 2014. World Ocean Atlas 2013, Volume 4: Dissolved Inorganic Nutrients (phosphate, nitrate, silicate). S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 76, 25 pp.

Gehlen, M., Bopp, L., Emprin, N., Aumont, O., Heinze, C. and Ragueneau, O. 2006: Reconciling surface ocean productivity,export fluxes and sediment composition in a global biogeochemical ocean model. Biogeosciences. 1726-4189/bg/2006-3-521,521-537.

Gehlen, M., Gangstø, R., Schneider, B., Bopp, L., Aumont, O. and Ethé, C. 2007: The fate of pelagic CaCO3 production in a highCO2 ocean: A model study. Biogeosciences., 4: 505-519.

Key, R. M., Kozyr, A., Sabine, C.L., Lee, K., Wanninkhof, R., Bullister, J.L., Feely, R.A., Millero, F.J., Mordy, C., and Peng, T.-H.2004: A global ocean carbon climatology: Results from Global Data Analysis Project (GLODAP). Global Biogeochem. Cycles. 18.GB4031, doi:10.1029/2004GB002247.

Landschutzer, P., N. Gruber and D.C.E. Bakker (2017). An updated observation-based global monthly gridded sea surface pCO2 and air-sea CO2 flux product from 1982 through 2015 and its monthly climatology (NCEI Accession 0160558). Version 2.2. NOAA National Centers for Environmental Information. Dataset.

Lellouche, J.-M., Le Galloudec, O., Drévillon, M., Régnier, C., Greiner, E., Garric, G., Ferry, N., Desportes, C., Testut, C.-E., Bricaud, C., Bourdallé-Badie, R., Tranchant, B., Benkiran, M., Drillet, Y., Daudin, A., and De Nicola, C.: Evaluation of real time and future global monitoring and forecasting systems at Mercator Océan, Ocean Sci. Discuss., 9, 1123-1185, doi:10.5194/osd-9-1123-2012, 2012.

Levier B, M G Sotillo, G. Reffray, R Aznar. 2016. CMEMS QUALITY INFORMATION DOCUMENT for IBI reanalysis Product: IBI_REANALYSIS_PHYS_005_002. CMEMS Technical Report (www.marine.copernicus.eu)

Longhurst, A. 1998: Ecological geography in the sea. Academic Press.

Ludwig, Wolfgang, Jean‐Luc Probst, and Stefan Kempe. "Predicting the oceanic input of organic carbon by continental erosion." Global Biogeochemical Cycles 10.1: 23-41 (1996).

http://www.marine.copernicus.eu/




Date : 3 April 2020

Issue : 3.2


Maréchal, D., 2004. A soil-based approach to rainfall-runoff modelling in ungauged catchments for England and Wales. PhD thesis, Cranfield University. 157 pp.

Masarie, K.A., P.P. Tans, (1995), Extension and integration of atmospheric carbon dioxide data into a globally consistent measurement record, J. Geopys. Research, vol. 100, 11593-11610.

Schneider, B., Bopp, L., Gehlen, M., Segschneider, J., Frölicher, T.L., Cadule, P., Friedlingstein, P., Doney, S.C., Behrenfeld M.J.and Joos, F. 2008: Climate-induced interannual variability of marine primary and export production in three global coupled climatecarbon cycle models. Biogeosciences. 5: 597-614

Schourup-Kristensen, V., Sidorenko, D., Wolf-Gladrow, D.A., and Völker, C. 2012. A skill assessment of the biogeochemical model REcoM2 coupled to the Finite Element Sea-Ice Ocean Model (FESOM 1.3). Geosci. Model Dev. 7: 2769–2802.

Séférian, Roland, et al. "Skill assessment of three earth system models with common marine biogeochemistry." Climate Dynamics 40.9-10 (2013): 2549-2573.

Sotillo M G, S. Cailleau, P. Lorente, B. Levier, R. Aznar, G. Reffray, A. Amo-Baladrón, J. Chanut, M. Benkiran E. Alvarez-Fanjul (2015): The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational products and roadmap to the future Copernicus Service, Journal of Operational Oceanography, DOI: 10.1080/1755876X.2015.1014663

Sotillo MG, B Levier, A Amo, S Cailleau. 2016. CMEMS PRODUCT USER MANUAL for Atlantic -Iberian Biscay Irish- Ocean Physics Reanalysis Product: IBI_REANALYSIS_PHYS_005_002. CMEMS Technical Report (www.marine.copernicus.eu)

Steinacher, M., Joos, F., Frölicher, T.L., Bopp, L., Cadule, P., Cocco, V., Doney, S.C., Gehlen, M., Lindsay, K., Moore, J.K.,Schneider, B., and Segschneider, J. 2010: Projected 21st century decrease in marine productivity: a multi-model analysis.Biogeoscience. 7: 979-1005.

Tagliabue, A., Bopp, L., Dutay, J.-C., Bowie, A.R., Chever, F., Jean-Baptiste, Ph., Bucciarelli, E., Lannuzel, Remenyi, D.T.,Sarthou, G., Aumont, O., Gehlen, M. and Jeandel, C. 2010: On the importance of hydrothermalism to the oceanic dissolved ironinventory. Nature Geoscience. 3: 252 – 256, doi:10.1038/ngeo818.

Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research 106(D7), 7183-7192.

http://www.marine.copernicus.eu/

atlantic ibi -iberian biscay irish- biogeochemical multi- year ......3.0 12/’02/2018 all update...

Documents