quo va dis? quarterly ocean validation display #1sam2 assimilating rtg-sst, sla from jason 1, jason...

Quo Va Dis ? Quarterly Ocean Validation Display #1, AMJ 2010

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Edition:

Charles Desportes, Marie Drévillon, Charly Régnier

(MERCATOR OCEAN/Production Dep./Products Quality)

Contributions :

Corinne Derval (CERFACS), Benoît Tranchant (CLS), Silvana Buarque (MERCATOR OCEAN),

Christine Boone (CLS), Stéphanie Guinehut (CLS), Gaëlle Nicolas (CLS)

Credits for validation methodology and tools:

Eric Greiner, Mounir Benkiran, Nathalie Verbrugge (CLS)

Charly Régnier, Fabrice Hernandez, Laurence Crosnier (MERCATOR OCEAN)

Jean-Michel Lellouche, Olivier Legalloudec, Gilles Garric (MERCATOR OCEAN)

Jean-Marc Molines (CNRS), Sébastien Theeten (Ifremer)

Nicolas Pene (AKKA)

Abstract

This bulletin gives an estimate of the accuracy of MERCATOR OCEAN’s analyses and forecast

for the season of April-May-June 2010. It also provides a summary of useful information on

the context of the production for this period. Diagnostics will be displayed for all MERCATOR

OCEAN’s monitoring and forecasting systems currently producing daily 3D temperature

salinity and current products. Finally we present a preliminary intercomparison of a few

physical processes viewed by the operational systems and by ORCA12 (with and without

data assimilation). The results show that the global ¼° and the Atlantic and Mediterranean

1/12° analyses and forecast still behave very similarly with an accuracy close to the expected

levels (as defined in scientific qualification documents), except for the 1/12° displaying

significantly better performance in the Mediterranean sea. Anyway this basin tends to be


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too warm in the model. The global 1/12° (in demonstration) displays at least as good

performance and especially less biases than the current systems.


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Table of contents

I Status and evolutions of the systems ................................................................................ 5

II Summary of the availability and quality control of the input data.................................... 5

II.1. Observations available for data assimilation ............................................................. 5

II.1.1. In situ observations of T/S profiles..................................................................... 5

II.1.2. Sea Surface Temperature................................................................................... 6

II.1.3. Sea Level Anomalies ........................................................................................... 7

II.2. Observations available for validation......................................................................... 7

III Information on the large scale climatic conditions............................................................ 7

IV Accuracy of the products ................................................................................................... 9

IV.1. Data assimilation performance .................................................................................. 9

IV.1.1. Sea surface height .............................................................................................. 9

IV.1.2. Sea surface temperature.................................................................................. 13

IV.1.3. Temperature and salinity profiles .................................................................... 17

IV.2. Accuracy of the daily average products with respect to observations.................... 19

IV.2.1. T/S profiles observations.................................................................................. 19

IV.2.2. Drifting buoys velocity measurements ............................................................ 25

IV.2.3. Sea ice concentration ....................................................................................... 25

V Forecast error statistics.................................................................................................... 26

V.1. Forecast accuracy: comparisons with observations when and where available..... 26

V.2. Forecast verification: comparison with analysis everywhere.................................. 29

V.2.1. Illustration ........................................................................................................ 29

V.2.2. Synthesis of one week forecast root mean square error (with respect to

analysis) 31

VI Monitoring of ocean and sea ice physics ......................................................................... 32

VI.1. Global mean SST and SSS ......................................................................................... 32

VI.2. Surface EKE............................................................................................................... 33

VI.3. Eddy kinetic energy in the North Atlantic: the Gulf Stream eddies at depth .......... 33

VI.4. Sea Ice extent area and volume ............................................................................... 34


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Table of figures

Figure 1 : Percentage of valid profiles as a function of their age. .......................................................................... 6

Figure 2: SST monthly anomalies (°c) at the global scale from the 1/4° ocean monitoring and forecasting

system PSY3V2R2 with respect to Levitus (2005) climatology. ................................................................. 8

Figure 3: Arctic sea ice extent from the NSIDC .................................................................................................... 9

Figure 4: Color code for the Atlantic ocean regional boxes were the data assimilation statistics are

computed. ................................................................................................................................................... 10

Figure 5: Synthesis of regional SLA (cm) average misfit (upper panels) and RMS misfit (lower panels) in

the Altantic Ocean. ..................................................................................................................................... 10

Figure 6: Synthesis of regional SLA (cm) average misfit (left panel) and RMS misfit (right panel) in the

Mediterranean Sea. ..................................................................................................................................... 11

Figure 7: regions for the computation of data assimilation statistics at the global scale...................................... 12

Figure 8 : Synthesis of regional SLA (cm) average misfit (upper panels) and RMS misfit (lower panels) in

the Global Ocean (except Atlantic and Mediterranean). ............................................................................ 13

Figure 9: Time series of SST (°C) data assimilation scores of misfit (observation – model) average and

RMS for PSY3 and PSY4 at the global scale and in the Antarctic and Nino 3regions .............................. 14


RMS for PSY2 in the Irminger Sea region of the North Atlantic. .............................................................. 15


RMS for PSY2 in the Mersa Matruh region (West of Alexandria) and in the Sicily region of the

Mediterranean Sea. ..................................................................................................................................... 16

Figure 12: Time series of temperature profiles (°C) average of innovation and RMS of innovation on the

global PSY3 and PSY4............................................................................................................................... 17

Figure 13: Time series of salinity profiles (°C) average of innovation (left column) and RMS of

innovation on the global PSY3 and PSY4................................................................................................. 18

Figure 14: Time series of temperature profiles (°C) and salinity profiles (psu) average of innovation and

RMS of innovation on the whole PSY2 geographical domain. .................................................................. 19

Figure 15: Upper panel: RMS temperature and salinity difference (model-observation) between all

available T/S observations from the Coriolis database and the daily average PSY3 products

colocalised with the observations. .............................................................................................................. 20

Figure 16: Upper panel: RMS temperature and salinity difference (model-observation) between all

available T/S observations from the Coriolis database and the daily average PSY2 products

colocalised with the observations ............................................................................................................... 21

Figure 17 : Water masses (Theta, S) diagrams in the Mediterranean Sea and Bay of Biscay, comparison

between PSY2 (left column) and PSY3 (right column).............................................................................. 22

Figure 18 : Water masses (Theta, S) diagrams in the Tropical and North Atlantic in PSY3 ............................... 23

Figure 19: Water masses (Theta, S) diagrams in the South Atlantic and Indian Ocean in PSY3 ........................ 24

Figure 20: PSY3 analyses of velocity (m/s) colocated with drifting buoys velocity measurements.................... 25

Figure 21: sea ice cover fraction (%) mean and RMS difference between CERSAT observations and

PSY3 sea ice cover in regional boxes in the Arctic Ocean. ........................................................................ 26

Figure 22: Time series of RMS difference between CERSAT sea ice cover fraction (%) and PSY3 in the

Greenland Basin region (left panel) and in the Barents Sea (right panel). ................................................. 26

Figure 23: In the North Atlantic region, time series of forecast accuracy at 3 and 6 days range, together

with analysis, persistency and climatology (Levitus (2005) and Arivo) accuracy ..................................... 27

Figure 24: same as Figure 23 for the Mediterranean sea and the PSY2 system, in the 0-500m layer.................. 27

Figure 25: same as Figure 23 for temperature only in the 0-500m layer, the PSY3 system ................................ 28

Figure 26 : comparison of the sea surface height (m) forecast – hindcast RMS differences for the 1 week

and 2 weeks ranges. On the left: for the PSY2 system, and on the right: for the PSY3 system. ................ 29

Figure 27 : comparison of the Tropical Atlantic 100m Temperature (°C ) RMS differences and of the

Mediterranean sea surface height (m) average difference of the 2 weeks forecast – hindcast ................... 30

Figure 28: Upper panel: Monthly and daily SST (°C) global mean for a one year period ending in AMJ

2010, for PSY3 and RTG-SST observations. Upper panel: same thing for sea surface salinity SSS......... 32

Figure 29: PSY3 surface eddy kinetic energy EKE (m²/s²), and RMS of Sea Surface Heigth SSH (m).............. 33

Figure 30: 48°N section of EKE (m²/s²) in the North Atlantic in all available analyses: PSY4, PSY3,

PSY2, and in a numerical experiment with no data assimilation (ORCA12). ............................................ 34

Figure 31: Sea ice extent and volume in PSY3 for a one year period ending in AMJ 2010. ............................... 34


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I Status and evolutions of the systems

PSY2V3R1 :

NATL12 LIM2 (Tropical, North Atlantic and Mediterranean Sea, 1/12° horizontal resolution,

50 vertical levels)

SAM2

Assimilating RTG-SST, SLA from Jason 1, Jason 2 and Envisat, in situ profile from CORIOLIS

Status : operated weekly, with daily updates of atmospheric forcing

PSY3V2R2 :

ORCA025 LIM2 (Global, 1/4° horizontal resolution, 50 vertical levels)

SAM2


Status : operated weekly

PSY4V1R3 :

ORCA12 LIM2 (Global, 1/12° horizontal resolution, 50 vertical levels)

SAM2 + Incremental Analysis Update (IAU)


Status : in demonstration mode, currently stopped

NB: for technical reasons, PSY4V1R3 is sometimes referred to as PSY4V1R2 in this document,

to be corrected.

This season, an update of the quality control of the input T/S profiles has been implemented.

Due to transfer problems the T/S profiles in the Indian Ocean were not assimilated in PSY3

on the 26th

of May.

II Summary of the availability and quality control of the input data

II.1. Observations available for data assimilation

II.1.1. In situ observations of T/S profiles

PSY2: between 300 and 700 temperature profiles and between 100 and 500 salinity profiles

are assimilated per analysis.

PSY3: between 1300 and 3500 temperature profiles and between 1000 and 2700 salinity

profiles are assimilated per analysis.

PSY4: between 1500 and 3700 temperature profiles and between 1200 and 2900 salinity

profiles are assimilated per analysis.

The number of profiles provided by Coriolis during the last quarter has decreased by 47%

with respect to the previous quarter, but this had no impact on our analyses, as the decrease

is due to a decrease in hourly data that are then undersampled during the quality check (to

remove redundant profiles and keep, at the most, one profile per 0.1° box every 24 hours).


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Still, two global gaps in in-situ measurements are noticed: from 25 to 27 April and from 22 to

25 May. During these days all kinds of measurements were affected and no measurement

was available. Otherwise, globaly, available observations are quite stable in quantity and

coverage, up to 2000 meters deep, mainly thanks to Argo network. Concerning the

Mediterranean Sea, observation network is very scarse (about 20 buoys, for the most part in

the first 600 meters).

Concerning the age of observations for the last three months: in average, most of the

observations are available between 2 and 8 days after the measurement. After 7 days, about

80% of the measurements are available (see Figure). After 30 days, every measurement is

available.

Figure 1 : Percentage of valid profiles as a function of their age. Left: for each observation type

separately, right: all types together. Statistics are computed for validated/under sampled

observations, available for April-May-June 2010 period. From “Rapport trimestriel de suivi des

observations T/S – Avril/Juin 2010”

II.1.2. Sea Surface Temperature

PSY2 : 29000 to 31000 observations are assimilated per analysis



The intercomparison of SST products as shown that RTG-SST has a cold bias in the Arctic and

in the Antarctic circumpolar current, see:

http://ghrsst-pp.metoffice.com/pages/latest_analysis/sst_monitor/daily/ens/index.html


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This bias was measured with the High Resolution (~1/10 °) version of the RTG-SST product,

while the observations assimilated by the systems come from the ½° resolution product, see:

http://polar.ncep.noaa.gov/sst/

These biases in the different SST products are currently under examination by the ARMOR

team at CLS.

II.1.3. Sea Level Anomalies

For PSY2: the order of magnitude is 15000 observations per satellite and per analysis, which

gives a total of 45000 observations per analysis.

PSY3 and PSY4: For each satellite the number of data assimilated per analysis in the global

systems is of the order of 90000, giving a total of the order of 250000-300000 observations

per cycle. PSY4 assimilates more data o(300000) than PSY3 o(250000)

There was a drop in the number of Jason 1 data assimilated in the analyses of June, 2nd

and

June, 9th (approximately divided by 3 on the 9th

)

II.2. Observations available for validation

Both observational data and statistical combinations of observations are used for the real

time validation of the products, most of them were available in real time during the season:

• T/S profiles

• OSTIA SST

• Arctic sea ice concentration

• Surcouf surface currents

• Armor-3D 3D temperature and salinity fields.

SST Odyssea SST maps (temporarily stopped) and Arctic sea ice drift products were not

available during this season, and the delivery of drifting buoys velocity measurements was

delayed several times.

III Information on the large scale climatic conditions

This season was characterized by the end of the El Niño atmospheric and oceanic

conditions, and by signatures of the premises of a La Niña phase. In the ocean (see surface

temperatures in Figure 2), the Eastern Tropical Pacific Ocean gets cooler, with negative

temperature anomalies at depth.

The Tropical Atlantic surface temperatures were anomalously warm through all the season.

This signal is also clear in the heat content over the first 300m of the ocean (not shown).

The North Atlantic oscillation is persistently negative, inducing a warming in the Gulf of

Mexico and Northernmost part of the Atlantic and a cooling in the centre of the North

Atlantic basin.

The Mediterranean Sea is anomalously warm in the eastern and southern parts.


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Figure 2: SST monthly anomalies (°c) at the global scale from the 1/4° ocean monitoring and

forecasting system PSY3V2R2 with respect to Levitus (2005) climatology. Upper panel April

anomaly, middle panel May anomaly, lower panel June anomaly.


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This season also sees the beginning of the seasonal melting of arctic Sea Ice. In June the sea

ice extent is below the minimum of 2007 as can be seen in Figure 3.

Figure 3: Arctic sea ice extent from the NSIDC:

http://nsidc.org/data/seaice_index/images/daily_images/N_stddev_timeseries.png

IV Accuracy of the products

IV.1. Data assimilation performance

IV.1.1. Sea surface height

IV.1.1.1. Tropical and North Atlantic Ocean synthesis for all systems

The Tropical and North Atlantic Ocean SLA assimilation scores of the current operational

systems PSY2 and PSY3 are displayed in Figure 5 together with the scores of the

demonstration system PSY4. The PSY2 system exhibits lower regional biases (of the order of

2cm) than PSY3, except for the small Florida Strait region. The bias is even less in the PSY4

system. The RMS error (order of magnitude 5-8 cm) is generally lower than the intrinsic

variability of the observations which indicates a good performance of the system in this

region (see Mercator Quarterly Newsletter #9). The RMS error is o(20cm) in regions of high

mesoscale variability like the Gulf Stream. In this case the ratio between the RMS of the

“observation-model” difference and the RMS of the observations is still lower than 1 (not

shown), indicating good performance of the data assimilation.


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Figure 4: Color code for the Atlantic ocean regional boxes were the data assimilation statistics are

computed.

Figure 5: Synthesis of regional SLA (cm) average misfit (upper panels) and RMS misfit (lower

panels) for PSY2 (left column), PSY3 (center column) and PSY4 (right column) in the Altantic Ocean.

Each region has a color code given in Figure 4. A value of average and RMS misfit is displayed as a

bar for each satellite Jason 1 (J1), Envisat (E) and Jason 2 (J2) (from bottom to top).


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IV.1.1.2. Mediterranean Sea by PSY2 (1/12°)

The Mediterranean Sea SLA assimilation scores of PSY2 (Figure 6) display a significant bias

towards a higher than observed Mediterranean basin (consistent with a warm bias). The

RMS error (order of magnitude 6-8 cm) is generally lower than the intrinsic variability of the

observations which indicates a good performance of the system in this region.

Figure 6: Synthesis of regional SLA (cm) average misfit (left panel) and RMS misfit (right panel) in

the Mediterranean Sea. Each region has a color code given in Figure 4. A value of average and RMS

misfit is displayed as a bar for each satellite Jason 1 (J1), Envisat (E) and Jason 2 (J2) (from bottom

to top).


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IV.1.1.3. Performance at global scale of PSY3 (1/4°) and PSY4 (1/12°)

Figure 7: regions for the computation of data assimilation statistics at the global scale, each color

and number corresponds to a different “box” region.


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Figure 8 : Synthesis of regional SLA (cm) average misfit (upper panels) and RMS misfit (lower

panels) for PSY3 (left column) and PSY4 (right column) in the Global Ocean (except Atlantic and

Mediterranean). A value of average and RMS misfit is displayed as a bar for each satellite Jason 1

(J1), Envisat (E) and Jason 2 (J2) (from bottom to top). The regions names from bottom to top

correspond respectively to numbers 23 to 46 on the map in Figure 7 (no color code).

In the global PSY3 ¼° system, the regional biases are of the order of 2 cm with a maximum of

4 cm near Chile coast (#42). These biases compensate at the global scale (axiom of the data

assimilation method).RMS errors stand between 5 and 8 cm, and reach more than 10 cm in

the high mesoscale variability currents: Agulhas (#29), circumpolar (#23) and Falkland (#25).

Bias is reduced in PSY4 with respect to PSY3 in almost all regions except the nino5 region

(#38, in the Indonesian Throughflow).

The RMS error is slightly lower in PSY4 than in PSY3, and is significantly reduced in the

circumpolar current.

IV.1.2. Sea surface temperature


The global average of the innovation (or misfit) observation – model differences in

Figure 9 shows a cold bias of 0.1°C in PSY3 and PSY4. Note that in the case of PSY4, this bias

can be reduced after the incremental analysis update (we show here the innovation: the

difference between the observations and the guess trajectory). In the Antarctic the model is

unbiased with respect to the RTG-SST data, but still displays a small 0.1 °C cold bias with

respect to in situ data in the surface layer. This is consistent with a possible cold bias of RTG-

SST in this region (to be followed). In the nino3 box, a significant cold bias of 0.5°C appears in

May-June with respect to RTG-SST, and is less clear with respect to in situ data. The intensity

of this bias thus exhibits seasonal or interannual variability as the cold anomaly appearing at

the end of the Nino episode of this winter is probably overestimated by the model.


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region PSY3 PSY4

Global

(#0)

Antarctic

(#23)


15

Nino

3

(#35)

Figure 9: Time series of SST (°C) data assimilation scores of misfit (observation – model) average

and RMS for PSY3 (left column) and PSY4 (right column) at the global scale (upper panel) and in the

Antarctic and Nino 3regions (middle and lower panels). In blue: in situ 5 m temperature

observations, in black: RTG-SST observations.

In the North Atlantic PSY2 and PSY3 give comparable SST assimilation scores. Figure 10

illustrates a possible cold bias of RTG-SST in this Northern region of the Atlantic (the number

of available in situ data may be too small to conclude).


and RMS for PSY2 in the Irminger Sea region of the North Atlantic. In blue: in situ 5 m temperature

observations, in black: RTG-SST observations.


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IV.1.2.2. Mediterranean Sea by PSY2 (1/12°)

The seasonal warming of the Mediterranean sea seems overestimated in PSY2 as shown by

Figure 11. A warm bias is intensifying in several regions at the end of June. The comparison

with OSTIA products (not shown) indicates that this overestimation is due to local warming

in small structures that are not resolved by the satellite products, and if realistic may not be

well located in PSY2.


and RMS for PSY2 in the Mersa Matruh region (West of Alexandria, left panel) and in the Sicily

region (right panel) of the Mediterranean Sea. In blue: in situ 5 m temperature observations, in

black: RTG-SST observations.


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IV.1.3. Temperature and salinity profiles


As can be seen in Figure 12 PSY3 is generally too cold over 100 m, and too warm (0.3

°C) between 100 and 200 m. A warm bias can be seen at depth. PSY4 is too cold (0.5 °C) over

the 0-500m water column, and the bias seems stronger in June. The warm bias at depth is

reduced compared to PSY3. In both systems the RMS error reaches 1.2°C near 100m at the

average thermocline position. Under 1000m the RMS error is lower in PSY4 (0.1 °C) than in

PSY3 (0.15 °C).

Figure 12: Time series of temperature profiles (°C) average of innovation (left column) and RMS of

innovation (middle column) on the global PSY3 (upper panel) and PSY4 (lower panel). In the right

column: RMS average over time


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Both systems display a salty bias near 100m and fresh bias near the surface (Figure

13). The fresh bias visible in PSY3 below 1000m disappears in PSY4, with consequently a

division by 2 of the RMS error from 0.05 psu in PSY3 to 0.025 in PSY4.

Figure 13: Time series of salinity profiles (°C) average of innovation (left column) and RMS of

innovation (middle column) on the global PSY3 (upper panel) and PSY4 (lower panel). In the right

column: RMS average over time

IV.1.3.2. Tropical and North Atlantic Ocean, Mediterranean Sea by

PSY2 (1/12°)

Due to a smaller sample PSY2 temperature and salinity biases (Figure 14) are amplified with

respect to the global domain averages of PSY3 and PSY4. A bias structure appears near 1000

1500m due to the ill positioned Mediterranean outflow in the Atlantic (currently this bias is

present in all systems).


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IV.2. Accuracy of the daily average products with respect to

observations

IV.2.1. T/S profiles observations

IV.2.1.1. Global statistics

As can be seen in Figure 15, temperature errors in the 0-500m layer stand between

0.5 and 1 °C in most regions of the globe. Regions of high mesoscale activity and regions of

Sea Ice melting experience higher values (up to 3°C). We note that in most regions there are

less than 30 profiles to compute the statistics for this three months period. The salinity RMS

errors are usually less than 0.2 psu but can reach high values in regions of high runoff

(Amazon, Sea Ice limit) or precipitations (SPCZ), and in regions of high mesoscale variability.

Figure 14: Time series of temperature profiles (°C, upper panel ) and salinity profiles (psu, lower

panel) average of innovation (left column) and RMS of innovation (middle column) on the whole

PSY2 geographical domain. In the right column: RMS average over time


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Figure 15: Upper panel: RMS temperature (left column) and salinity (right column) difference

(model-observation) between all available T/S observations from the Coriolis database and the

daily average PSY3 products (here the nowcast run) colocalised with the observations. Lower

panel: number of data used to compute the statistic of the upper panel, by regional box.

If we compare Figure 15 and Figure 16 we note that PSY2 temperature RMS errors are

smaller in the Gulf Stream region and in the North Brazil current.

PSY2 salinity errors are lower than PSY3 errors in the Mediterranean and in the Bay of

Biscay, but they are higher in the Gulf Stream (high variability).


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Figure 16: Upper panel: RMS temperature (left column) and salinity (right column) difference

(model-observation) between all available T/S observations from the Coriolis database and the

daily average PSY2 products (here the nowcast run) colocalised with the observations. Lower

panel: number of data used to compute the statistic of the upper panel, by regional box.

IV.2.1.2. Water masses diagnostics

We use here the daily products (analyses) colocated with the T/S profiles to draw “theta, S”

diagrams. PSY2 better represents water masses characteristics in the Mediterranean, and

there is a slight improvement in the Bay of Biscay (Figure 17).


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Figure 17 : Water masses (Theta, S) diagrams in the Mediterranean Sea and Bay of Biscay,

comparison between PSY2 (left column) and PSY3 (right column)

In the Tropical and north Atlantic, PSY3 and PSY2 have very similar behaviours, we show

here PSY3 (Figure 18). In the tropics the systems stick to the climatology.


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Figure 18 : Water masses (Theta, S) diagrams in the Tropical and North Atlantic in PSY3

In the South Atlantic and Indian (Figure 19) the water masses are well described by the

climatology, the system captures some of the small changes seen by the observations.


24

Figure 19: Water masses (Theta, S) diagrams in the South Atlantic and Indian Ocean in PSY3


25

IV.2.2. Drifting buoys velocity measurements

The surface velocity is globally underestimated by the systems, as illustrated in Figure 20 by

comparisons of surface drifter velocity measurements with PSY3 velocities (comparisons

done at 15m). The relative error is approximately 20 % and reaches locally 50 %. About 40%

of the direction errors are lower than 45° and about 20% of the observed velocities have a

nearly opposite direction in the PSY3 analyses. These large direction errors are localized and

generally correspond to ill positioned mesoscale structures.

IV.2.3. Sea ice concentration

The melting of Sea Ice induce large differences between PSY3 and the observed sea ice cover

fraction, especially in the Bering Sea, Barents Sea, Greenland Basin and Labrador Sea (Figure

21). The sea ice doesn’t melt enough in the model. The RMS error is large in the Canadian

Archipelago where the model does not reproduce the variability of sea ice cover.

Figure 20: PSY3 analyses of velocity (m/s) colocated with drifting buoys velocity measurements. Upper left

panel : difference modele - observation of velocity module. Upper right panel: relative error of velocity

module (%). Lower left panel: direction errors of the velocity vector (°). Lower right panel: probability density

function of the direction errors (°).


26

Figure 21: sea ice cover fraction (%) mean and RMS difference between CERSAT observations and PSY3 sea

ice cover in regional boxes in the Arctic Ocean.

The RMS error time series (Figure 22) in the Greenland basin shows that the error increases

while the ice cover fraction decreases in May and June. In the Barents Sea the RMS error

does not increase, consistent with an observed sea ice extent less variable in this region.

Figure 22: Time series of RMS difference between CERSAT sea ice cover fraction (%) and PSY3 in the

Greenland Basin region (left panel) and in the Barents Sea (right panel).

V Forecast error statistics

V.1. Forecast accuracy: comparisons with observations when and

where available

As can be seen in Figure 23 the PSY3 and PSY2 products have a better accuracy than the

climatology in the North Atlantic region. The accuracy is higher in the near surface layer (0-

50m) than in the 0-500m layer. The analysis is the best product, but the RMS error of the

forecast is already approximately half that of the climatologies in the 0-50m layer. PSY2 has

the best analysis quality in the region, which can be seen especially on the 0-500m layer

diagnostics.


27

Figure 23: In the North Atlantic region, time series of forecast (FRCST) accuracy at 3 and 6 days

range, together with analysis (ANA and HDCST), persistency (PERS) and climatology (TMLEV Levitus

(2005) and TMARV Arivo from Ifremer) accuracy as measured by a RMS difference with respect to

all available temperature (°C) observations from the CORIOLIS database. Upper panel for the 1/12°

North Atlantic and Mediterranean system PSY2, lower panel for the ¼° global PSY3. Left column for

the 0-50m layer, right column for the 0-500m layer.

Figure 24: same as Figure 23 for the Mediterranean sea and the PSY2 system, in the 0-500m layer. On the left

temperature (°C) and on the right salinity (psu)


28

In the Mediterranean Sea PSY2 analysis are the more accurate products with respect to

climatology, persistence and forecast, especially in temperature (Figure 24). The forecast

RMS errors are slightly larger but still far below the error levels of the climatologies,

especially in April and May.

PSY3 statistics in the Atlantic, Pacific and Indian basin in the 0-500m layer (Figure 25) display

a generally good accuracy and added value of the analyses and forecast with respect to

climatology, especially in the Tropical Pacific. In this region the system is controlled by the

TAO/TRITON array of T/S moorings.

Figure 25: same as Figure 23 for temperature only in the 0-500m layer, the PSY3 system and the South

Atlantic Ocean (upper left panel), the Tropical Atlantic (upper right panel), the Tropical Pacific (lower left

panel) and the Indian Ocean (lower right panel).


29

V.2. Forecast verification: comparison with analysis everywhere

V.2.1. Illustration

The “forecast errors” illustrated by the sea surface height RMS difference between the

forecast and the hindcast for all given dates of the season AMJ 2010 are displayed in Figure

26. The results on the North Atlantic domain are very similar in PSY3 and PSY2 (o(2 cm)),

reaching the same order of maximum values in the regions of highest variability (o(20 cm)).

PSY2 SSH RMS diff of 1week forecast – hindcast

AMJ 2010

PSY3 SSH RMS diff of 1week forecast – hindcast

AMJ 2010

PSY2 SSH RMS diff of 2weeks forecast – hindcast

AMJ 2010

PSY3 SSH RMS diff of 2weeks forecast – hindcast

AMJ 2010

Figure 26 : comparison of the sea surface height (m) forecast – hindcast RMS differences for the 1

week (upper panels) and 2 weeks (lower panels) ranges. On the left: for the PSY2 system, and on

the right: for the PSY3 system.


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This similarity holds for all the comparisons that were made in the Atlantic and

Mediterranean regions, except for the 100 m temperature and circulation in the Tropical

Atlantic and for the SSH bias in the Mediterranean, as illustrated in Figure 27.

The North Brazil current is probably more realistic in PSY2, thanks to a better analysis in the

Amazon region and downstream in the North Brazil Current. The difference between PSY3

and PSY3 “forecast-hindcast” fields thus can come mainly from a different analysis in this

region (see Figure 15 and Figure 16).

In the Mediterranean Sea, PSY2 overall data assimilation performance and model skill are

better than PSY3’s, consistently PSY2 forecast is more accurate.

PSY2 T 100m RMS diff of 2 weeks forecast – hindcast

AMJ 2010

PSY3 T 100m RMS diff of 2 weeks forecast – hindcast

AMJ 2010

PSY2 SSH average diff of 2 weeks forecast – hindcast

AMJ 2010

PSY3 SSH average diff of 2 weeks forecast – hindcast

AMJ 2010

Figure 27 : comparison of the Tropical Atlantic 100m Temperature (°C, upper panels ) RMS

differences and of the Mediterranean sea surface height (m, lower panels) average difference of

the 2 weeks forecast – hindcast for all dates of the AMJ 2010 season. On the left: for the PSY2

system, and on the right: for the PSY3 system.


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V.2.2. Synthesis of one week forecast root mean square error (with respect

to analysis)

For all regions corresponding to the main ocean basins, forecast error estimated with the

RMS error of forecast-hindcast differences are reported in Table 1. These figures are meant

to give a rough idea of the forecast error for a given product/basin and will be monitored in

time.

Note that for the arctic region, comparisons are very good because there is no data

assimilation in this region (no data assimilation if sea ice). Thus the analysis and the forecast

for one given day differ only by the quality of the atmospheric forcing. Where the ocean is

covered by sea ice this influence is less important than in other major ocean basins.

Local maximum value of forecast errors are encountered in the high variability regions listed

in the following:

• North Atlantic: Gulf Stream, Labrador Current, Azores current, North Equatorial

current and counter current

• Tropical Atlantic: North Equatorial current and counter current

• South Atlantic: Zapiola Eddy, Aghulas current

• Mediterranean: Gulf of Lion

• Indian: Equatorial currents, Aghulas current , Western Australian coast, Madagascar

channel

• North Pacific: Equatorial, Kuroshio, Alaska

• South Pacific: East Australian Current

• Antarctic Circumpolar current.

region Region long name Variable (unit) Usual value

over the region

Local maximum value

NAT (PSY2/PSY3) North Atlantic T (K) at 0m 0.3/0.3 1. to 2.6/1.2 to 3.3

T (K) at 100m 0.3/0.2 0.6 to 2./1. to 3.

Mixed layer depth (m) 20/20 85 to 380/90 to 270

U (m/s) at 0 m 0.09/0.06 0.2 to 0.45/0.2 to 0.45

V (m/s) at 0 m 0.09/0.06 0.1 to 0.55/ 0.15 to 0.5

SSH (m) 0.015/0.015 0.05 to 0.2/0.05 to 0.2

TAT (PSY2) Tropical Atlantic T (K) at 0m 0.2 Up to 1

T (K) at 100m 0.3 Up to 1

Mixed layer depth (m) 4 Up to 30

U (m/s) at 0 m 0.09 0.2 to 0.4

V (m/s) at 0 m 0.06 Up to 0.3

SSH (m) 0.005 Up to 0.1

MED (PSY2) Mediterranean Sea T (K) at 0m 0.3 0.5 to 0.9

T (K) at 100m 0.06 0.2 to 0.5

Mixed layer depth (m) 20 Up to 160

U (m/s) at 0 m 0.06 Up to 0.2

V (m/s) at 0 m 0.06 Up to 0.2

SSH (m) 0.005 Up to 0.05

IND (PSY3) Indian Ocean T (K) at 0m 0.2 0.5 to 1.5

T (K) at 100m 0.4 0.5 to 1.5

Mixed layer depth (m) 15 50 to 60

U (m/s) at 0 m 0.09 0.1 to 0.4

V (m/s) at 0 m 0.09 0.1 to 0.4


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SSH (m) 0.02 0.07 to 0.2

ARC (PSY3) Arctic Ocean T (K) at 0m 0.06 0.2 to 1.7

T (K) at 100m 0.06 0.2 to 1.7

Mixed layer depth (m) 4 100 to 400

U (m/s) at 0 m 0.03 0.1 to 0.3

V (m/s) at 0 m 0.03 0.1 to 0.3

SSH (m) 0.015 0.03 to 0.15

NPA (PSY3) North Pacific Ocean U (m/s) at 0 m 0.09 0.1 to 0.4

V (m/s) at 0 m 0.06 0.1 to 0.5

SPA (PSY3) North Pacific Ocean U (m/s) at 0 m 0.06 0.1 to 0.3

V (m/s) at 0 m 0.06 0.1 to 0.3

TPA (PSY3) North Pacific Ocean U (m/s) at 0 m 0.09 0.1 to 0.3

V (m/s) at 0 m 0.09 0.1 to 0.3

ACC (PSY3) Antarctic Ocean U (m/s) at 0 m 0.13 0.2 to 0.6

V (m/s) at 0 m 0.13 0.2 to 0.6

Table 1: forecast errors summary, estimated with RMS errors and average errors of forecast-hindcast

differences over the period AMJ 2010 and in different regions (main ocean basins). For each region target

products are selected (subsample of available products).

VI Monitoring of ocean and sea ice physics

VI.1. Global mean SST and SSS

A global mean cold bias is diagnosed with respect to RTG-SST observations. At each analysis

cycle, the model tends to cool down after each analysis, as can be seen in Figure 28. Data

assimilation shocks are also visible in the SSS time series.

Figure 28: Upper panel: Monthly (left column) and daily (right column) SST (°C) global mean for a one year

period ending in AMJ 2010, for PSY3 (in black) and RTG-SST observations (in red). Lower panel: same thing

for sea surface salinity SSS for PSY3 (no corresponding observations).


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VI.2. Surface EKE

Regions of high mesoscale activity are diagnosed in Figure 29: Kuroshio, Gulf stream, Nino 3

box in the Equatorial pacific, Indian Equatorial current, Zapiola eddy, Agulhas current, East

Australian current, Madagascar channel etc… The signature of atmospheric forcing is also

visible in the circumpolar current in the RMS of SSH (Pacific and Indian quadrant).

Figure 29: PSY3 surface eddy kinetic energy EKE (m²/s²) (left panel), and RMS of Sea Surface Heigth SSH (m)

(right panel) for AMJ 2010.

VI.3. Eddy kinetic energy in the North Atlantic: the Gulf Stream eddies

at depth

The Gulf Stream EKE signature is visible at depth, crossing the North Atlantic at 48°N. We

compare in Figure 30 the monitoring and forecasting systems in AMJ 2010 (PSY2, PSY3 and

PSY4) with an ORCA12 experiment with no data assimilation in AMJ 2000.

As expected the levels of energy are higher in the 1/12° configurations, and PSY4 and PSY2

seem to behave very similarly.

ORCA12 with no DA (in 2000)

ORCA12 with DA -> PSY4


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ORCA025 with DA -> PSY3

ATL12 with DA -> PSY2

Figure 30: 48°N section of EKE (m²/s²) in the North Atlantic in all available analyses : PSY4 (upper right), PSY3

(lower left), PSY2 (lower right), and in a numerical experiment with no data assimilation (ORCA12, upper

left). NB: the PSY4 bathymetry difference is due to a graphical bug as well as the false surface values, under

investigation.

VI.4. Sea Ice extent area and volume

The ice extent in the Arctic reaches 11000 109 m

2 which is close to the June 2010 estimate of

NSIDC and below the 13000 109 m

2 climatological value. In the Antarctic the PSY3 ice extent

is lower than NSIDC climatology which is not realistic (the cover is currently wider than the

NSIDC extent).

Figure 31: Sea ice extent and volume in PSY3 for a one year period ending in AMJ 2010. Left column: sea ice

extent in 109 m

2 in the Arctic (upper panel) and Antarctic (lower panel) for PSY3 (black) and NSIDC

climatology (in red). Right column: Ice volume in the Arctic (upper panel) and Antarctic (lower panel) for

PSY3 (black).

quo va dis? quarterly ocean validation display #1sam2 assimilating rtg-sst, sla from jason 1, jason...

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