QUALITY INFORMATION DOCUMENT For Arctic Physical Reanalysis Product ARCTIC REANALYSIS ... · 2020-04-09 · QUALITY INFORMATION DOCUMENT For Arctic Physical Reanalysis Product ARCTIC_REANALYSIS_PHY_002_003

QUALITY INFORMATION DOCUMENT

For Arctic Physical Reanalysis Product

ARCTIC_REANALYSIS_PHY_002_003

Issue: 2.9

Contributors: Jiping Xie, Pavel Sakov, François Counillon, Laurent Bertino, Roshin Raj, Vidar S. Lien

Approval Date: 21/02/2020

QUID for Arctic Ocean Physical Reanalysis

Product

ARCTIC_REANALYSIS_ PHY_002_003

Ref: CMEMS-ARC-QUID-002-003

Date : 15th Feb 2020

Issue : 2.9

© EU Copernicus Marine Service – Public Page 2/ 59

CHANGE RECORD

Issue Date § Description of Change Author Validated By

1.0 14 January

2013

All

Creation of the document.

Slightly modified from the MyO

V2 QuID

Laurent Bertino

Pavel Sakov

François Counillon

1.1 1st March

2013

All Correction after QuARG review Laurent Bertino

Pavel Sakov

François Counillon

1.2 30th

June

2014

I.3,

II.4

IV

EAN updated

History reconstructed from logs

Added several validation results

(tide gauges, transports…)

Christoph Renkl,

François Counillon,

Nicolas Finck

Laurent Bertino

1.3 3rd

November

2014

Correction after QuARG review Laurent Bertino

2.0 18th

December

2014

Longer time series Laurent Bertino

2.1 2nd

March

2015

All Revision after V5 acceptance Laurent Bertino

2.2 May 1 2015 All

Change format to fit CMEMS

graphical rules

L. Crosnier

2.2 26 Jan 2016

IV.2.4

IV.2.5

IV.2.6

Longer time series

Added validations for sea ice

thickness

For sea ice drift

For mixed layer thickness

Jiping Xie

Roshin Raj

2.3 18 Jan 2017 II

IV 2.4

IV.2.8

Updated description

Update sea ice volume

Added example of OMI

Laurent Bertino

Jiping Xie

Vidar S. Lien

2.4 18th Mar 17 IV.1 Longer time series 2014-2015 Jiping Xie

2.5 21th Nov 17 II.1

IV.1

Assimilating the CS2SMOS into

the system during 2014-2016;

updates the longer time series

2014-2016

Jiping Xie

2.6 5th Sep 18 II.1

IV.1

VI

Illustration of the CS2SMOS

product and the tuned

observation error term

Comparison to assimilated data

Modification compared to

previous version

Jiping Xie L. Bertino

2.7 2nd Jul 19 II.4 Added year 2018 (interim) L. Bertino Mercator Ocean

2.8 6th Nov 19 II.4

IV

Updated year 2018

J. Xie Mercator Ocean

2.9 15 Feb 20 II.1 & V L. Bertino Mercator Ocean


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TABLE OF CONTENTS

I Executive summary ....................................................................................................................................... 4

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

I.2 Summary of the results ............................................................................................................................... 4

I.3 Estimated Accuracy Numbers .................................................................................................................... 5

II Production Subsystem description ................................................................................................................ 7

II.1 Assimilated data ......................................................................................................................................... 7

II.2 The Model: ................................................................................................................................................ 10

II.3 Data assimilation ...................................................................................................................................... 12

II.4 History of the reanalysis .......................................................................................................................... 13

III Validation framework ............................................................................................................................. 15

IV Validation results ......................................................................................................................................... 20

IV.1 Assimilation diagnostics ......................................................................................................................... 20

IV.1.1 Sea level anomalies ..................................................................................................................... 20

IV.1.2 Sea surface temperatures............................................................................................................. 24

IV.1.3 Sea ice concentrations ................................................................................................................. 29

IV.1.4 Temperature profiles ................................................................................................................... 32

IV.1.5 Salinity profiles ........................................................................................................................... 35

IV.1.6 Relative impact of each data type ............................................................................................... 38

IV.2 Validation against independent data .................................................................................................... 39

IV.2.1 Tide gauges ................................................................................................................................. 39

IV.2.2 Current velocities ........................................................................................................................ 42

IV.2.3 Surface Heat Fluxes .................................................................................................................... 44

IV.2.4 Sea ice thickness and Ice volume ................................................................................................ 47

IV.2.5 Sea ice drift ................................................................................................................................. 49

IV.2.6 Mixed layer thickness in Nordic Sea .......................................................................................... 52

IV.2.7 Moorings in the Fram Strait ........................................................................................................ 53

IV.2.8 Volume transports ....................................................................................................................... 54

V Quality changes since previous version ...................................................................................................... 57

VI References .................................................................................................................................................... 58


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I EXECUTIVE SUMMARY

I.1 Products covered by this document

The CMEMS product described here is referred as ARCTIC_REANALYSIS_PHY_002_003

I.2 Summary of the results

The TOPAZ4 reanalysis was assessed in the period 1991-2018. Class4 metrics have been

computed in the Arctic and Nordic boxes against assimilated data to verify the reanalysis

system was stable. The classical Kalman Filter diagnostics have been used to monitor the

assimilation of each observation source and their relative contribution to the overall

information flow. The main validation emphasis was given to assimilated data.

The Ensemble Kalman Filter (EnKF) data assimilation system used in the Arctic MFC has

proven mature in the sense that no drift was visible from the assimilation statistics (no

collapse of the ensemble, no visible trend in the biases).

The first merged sea ice thickness from Cryosat2 and SMOS is available since October 2014.

It is named CS2SMOS (Ricker et al., 2017) and released with weekly frequency. In addition,

the OSTIA SST did correct a bug of the NRT product on the 5th February 2014, its neglection

in the present reanalysis results in an underestimation of the SST observations error in the

assimilation system. The RMS differences of SST and ICEC therefore increase by 50% in the

year 2014 shown in the last year. Consequently, we reprocessed the reanalysis after 2014-

2015, and began to assimilate the CS2SMOS data into the lastfour years. The assimilated

temp/salt profiles from CORA5.2 are involved more types with an increased localization

radius from 300 km to 750 km.


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I.3 Estimated Accuracy Numbers

Table 1: Root-Mean-Squared Difference (RMSD)

Variable Reference data Period Details Reanalysis Free Run

Temperature World Ocean Atlas 2013

1991-2013 0 m 0.52º C 0.66º C

100 m 0.70º C 0.88º C

300 m 0.92º C 1.22º C

800 m 0.55º C 0.44º C

2000 m 0.34º C 0.28º C

SST OSTIA 1999-2013 w/o bias red. 0.51º C 1.05º C

inst. bias red. 0.43º C

last bias red. 0.48º C

Salinity World Ocean Atlas 2013

1991-2013 0 m 1.55 PSU 1.53 PSU

100 m 0.33 PSU 0.61 PSU

300 m 0.10 PSU 0.20 PSU

800 m 0.04 PSU 0.04 PSU

2000 m 0.06 PSU 0.05 PSU

Sea level anomalies

PSMSL tide gauges

1991-2013 Norwegian Sea 0.089 m 0.085 m

Baltic Sea 0.134 m 0.118 m

Kara Sea and Laptev Sea

0.077 m 0.074 m

East Siberian Sea 0.107 m 0.105 m


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Table 2: Trends

Variable Period Data Trend

SST 1999-2013 TOPAZ Reanalysis w/o bias reduction 0.022º C/yr

TOPAZ Reanalysis inst. bias reduction 0.023º C/yr

TOPAZ Free Run 0.008º C/yr

OSTIA 0.025º C/yr

SSS 1991-2013 TOPAZ Reanalysis 0.001 PSU/yr

TOPAZ Free Run 0.005 PSU/yr

SLA 1991-2013 TOPAZ Reanalysis 0.002 m/yr

TOPAZ Free Run 0.002 m/yr


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II PRODUCTION SUBSYSTEM DESCRIPTION

The ARC-MFC nominal system is the TOPAZ system based on an advanced sequential data

assimilation method (the Ensemble Kalman Filter, EnKF) in its deterministic flavour

(DEnKF, Sakov and Oke, 2009) and the Hybrid Coordinate Ocean Model (HYCOM version

2.2). This report describes the 27-years Arctic reanalysis product in the period 1991-2018

included. The variables delivered are all physical variables, including 3D currents,

temperatures and salinities, 2D parameters for sea ice, mixed layer depth and sea surface

heights. Sea surface temperature and sea surface heights are corrected for bias, with an online

bias correction algorithm.

- Production centre name: Arctic Marine Forecasting Centre. ARC-MFC

- Production subsystem name: TOPAZ4

- Production centre description: Nansen Center, Bergen, Norway. NERSC-BERGEN-

NO

- Name in the catalogue: ARCTIC_REANALYSIS_PHY_002_003

- Dataset names dataset-arc-nersc-bergen-no-myocean-rv2 and dataset-arc-nersc-

bergen-no-myocean-rv2-day

II.1 Assimilated data

Observations that are assimilated by TOPAZ4 include along-track Sea Level Anomalies

(SLA) from satellite altimeters, Sea Surface Temperature (SST) from NOAA and then the

Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), in situ temperature

and salinity from hydrographic cruises and moorings, ice concentrations (ICEC) from

OSI-SAF, the CS2SMOS1 merged sea ice thickness (SIT) from Cryosat2. The system uses a

7-day assimilation cycle, and assimilates the gridded SST and ICEC for the day of the

analysis; and along-track SLA, SIT and in-situ T and S for the week prior to the day of the

analysis. A brief overview of observations used in the reanalysis is given in Table 1.

Quality control procedures and preprocessing steps include a range check and horizontal

superobing. The details for each observation type follow.

1 (http://data.meereisportal.de/maps/cs2smos/version3.0/n)

http://mis-gw.nersc.no/mis-gateway-servlet/Motu?action=productdownloadhome&service=http%3A%2F%2Fpurl.org%2Fmyocean%2Fontology%2Fservice%2Fdatabase%23ARCTIC_REANALYSIS_PHYS_002_003-FILE&product=http%3A%2F%2Fpurl.org%2Fmyocean%2Fontology%2Fproduct%2Fdatabase%23dataset-arc-nersc-bergen-no-myocean-rv2




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Table 3: Overview of assimilated observations per each cycle, average numbers for the cycles during which the observations are present.

Type Number After Superobing

Spacing Period Product name

SLA 9·104 4·10

4 Track 1993-2018 SEALEVEL-GLO-SLA-L3-RAN-

OBSERVATIONS-008-001-b

SST

(Reynolds)

6·103 6·10

3 Gridded 1990-1998 External product

SST

(OSTIA)

2·106 2.2 · 10

5 Gridded 1998-2018 SST-GLO-SST-L4-RAN-OBSERVATIONS-

010-011-b

In-situ T 3·104 5. 10

3 Point 1991-2018 INSITU-GLO-TS-RAN-OBSERVATIONS-013-

001-b and (after June 2018).

In-situ S 3·104 5. 10

3 Point 1991-2018 INSITU-GLO-TS-RAN-OBSERVATIONS-013-

001-b and (after June 2018)

Ice conc.

(SSM/I)

9 · 104 5. 10

4 Gridded 1990-2018 SEAICE-GLO-SEAICE-TIMESERIES-RAN-

OBSERVATIONS-011-009

Ice drift 5.103 5.10

3 Gridded 2002-2018 SEAICE_ARC_SEAICE_L3_REP_OBSERVAT

IONS_011_010 (-> 2011) then

SEAICE_GLO_SEAICE_L4_NRT_OBSERVA

TIONS_011_001

Sea ice

thickness

1.104 1.10

4 Gridded 2014-2018 http://data.meereisportal.de/maps/cs2smos/v

ersion3.0/n

Total 2.2 · 106 3·10

5

- The altimetry data used for assimilation are the along- track SLA (SEALEVEL-GLO-

SLA-L3-RAN-OBSERVATIONS-008-001-b) from TOPEX/Poséidon, ERS1,

JASON-1, JASON-2, ENVISAT provided by the CMEMS SeaLevel TAC from

September 1992 to 2010. These data are geophysically corrected for tides, inverse

barometer, tropospheric, and ionospheric signals [Le Traon and Ogor, 1998; Dorandeu

and Le Traon, 1999]. The oceanographic signal is less accurate near the coast because

of pollution by land and in shallow waters due to inaccuracies of the global tidal

model that is used to de-alias the along-track altimeter observations. Therefore, we

only retain data located both in water deeper than 200 m and at least 50 km away from

the coast. The observation error is a combination of instrumental and representation

error, where the instrumental error is set as recommended by the provider (3 or 4 cm

depending on the satellite), and the representation error accounts for sub-grid

variability of observations. Little is known about the latter and we assume that this

error is larger in the more dynamical areas [Oke and Sakov, 2008]. Thus, a proxy

based on the model variance for the period 1993-1999 scaled by a factor of 0.7 is used.

The observations are assimilated asynchronously [Sakov et al., 2010] by using daily

snap-shots of the ensemble SLA fields.

- The SST data assimilated is sourced from OSTIA [OSTIA Stark et al., 2007], a

CMEMS product SST-GLO-SST-L4-RAN-OBSERVATIONS-010-011-b. The data

set was included from June 1998 at horizontal resolution of approximately 6 km

(though the spatial scales evident in OSTIA tend to be significantly coarser than 6

km), and is free of diurnal variation. It is a foundation SST product that combines data

from infrared sensors (AVHRR and AATSR), microwave sensors (AMSR-E and

http://data.meereisportal.de/maps/cs2smos/version3.0/n

http://data.meereisportal.de/maps/cs2smos/version3.0/n


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TMI), and in situ data from ships and surface drifting buoys. From the initial data set,

the values retained include those that are within a realistic range (i.e. ∈ [-1.9, 45]◦ C)

and away from the ice edge (mask provided with OSTIA data). The observation error

estimated by the provider is purposely overestimated by a factor 2.5 to account for the

representation error. Prior to June 1998, TOPAZ4 uses version 2 of the Reynolds SST

product [Reynolds and Smith, 1994] from the National Climatic Data Center (NCDC),

which has a resolution of approximately 100 km.

- Temperature (T) and salinity (S) profiles from research cruises that are assimilated

from January 1991 to 2013 were downloaded from the CMEMS InSitu TAC: INSITU-

GLO-TS-RAN-OBSERVATIONS-013-001-b. Unlike SLA data, in situ temperature

and salinity data are not assimilated asynchronously, and are instead assumed to

correspond to the analysis time, even though they spanned the week preceding the

analysis time. Profiles of T and S are checked for hydrostatic stability, and

observations within each profile are superobed vertically to retain a maximum of one

super-observation per layer, based on the layer structure of the first ensemble member.

The forecast at each observation for each ensemble member is calculated by linearly

interpolating between the adjacent layers of each member to the depth of the

observation. The scientific cruise data from the World Ocean Atlas [WOA05 Levitus

et al., 2005, WOA09], ICES, IOPAS, IMR, AARI, Ocean Weather Station Mike,

NABOS, NPI, North Pole Environment Observatory, the TRACTOR project, MMBI,

LOGS are also assimilated after being manually quality checked (A. Korablev and A.

Smirnov, pers. com.). A total of 3.000.000 observations are assimilated.

- The ICEC data is obtained from SSM/I at the SIW-TAC (OSI-SAF data SEAICE-

GLO-SEAICE-TIMESERIES-RAN-OBSERVATIONS-011-009). It is computed with

the ARTIST sea ice concentration algorithm. The gridded data is available from 1979

to 2013 and has been assimilated since 1990, at a resolution of 25 km. The spatial

coverage is almost complete. TOPAZ4 assimilates the ICEC data on the day of each

analysis. The observation error standard deviation is set to 10% at the start of the

reanalysis and is increased to account for larger errors near the ice edge and to reduce

over-fitting at these locations. The error variance is: σ2obs = 0.01 + (0.5 − |0.5 − c|)

2,

where c is the observed ICEC.

- The sea ice drift product is provided by CERSAT, Ifremer [Ezraty et al., 2006]. The

Lagrangian drift data is obtained at a resolution of 35 km by a pattern recognition

algorithm from QuickSCAT, AMSR-E and SSM/I images. It is available from

October to April inclusive and does not provide information close to the ice edge. The

3-day drift has been chosen as a compromise: long enough to average out some

random errors in the composites that are computed over shorter periods and short

enough to avoid severe loss of data near the coast that occurs in the composites

computed over longer periods. The data is available from October 2002 but the data is

unavailable during summer due to loss of patterns where melting occurs. The provider

accuracy estimate of 7 km/3 days is overestimated by a factor 2 to account for

representation error. Because the sea ice drift data is Lagrangian, the corresponding

observation operator is nonlinear. The model equivalent 3-days drift is computed for

each ensemble member and each grid cell of the satellite data product. The initial

positions are advected 3 days forward using model daily averaged ice velocities and a


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2nd order Runge-Kutta method. The final displacements are computed on the

observation grid. To the best of our knowledge, assimilation of ice drift in TOPAZ

represents the first example of assimilating Lagrangian data in a realistic ocean model.

In Oct 2011, the OSI SAF 2-days drift replaces the CERSAT product.

- - The sea ice thickness product is provided by http://www.meereisportal.de (Ricker et

al., 2017). It is a merged product of weekly SIT measurements in Arctic from the

CryoSat-2 altimeter and SMOS radiometer (referred to as CS2SMOS). This product is

gridded with a resolution of approximately 25 km. The provider uses optimal

interpolation to blend the measurements of CryoSat-2 and SMOS based on the best

estimate, their uncertainties and their spatial covariance. An estimate of the

observation error is provided with the data set but it only accounts for the errors

related to the merging and interpolation (Ricker et al., 2017). In order to estimate the

representation error for the SIT observation, we used the method proposed by

Desroziers et al. (2005) to evaluate the observation error suitable in the TOPAZ4

system for assimilating CS2SMOS data in a short sensitivity experiment. we have

added a term to the C2SMOS raw error estimate, which increases with the amplitude

of SIT: =min(0.5, 0.1+0.15*d), where d represents the merged SIT measurement.

Using the evaluation work of SIT from CryoSat-2 (Tilling et al, 2018), the

aforementioned maximal observation error is limited by a threshold value of 0.5 m in

the years of 2014-2015 only. Afterwards, based on the evaluation of assimilating the

SIT in Xie et al. (2018), the additional observation error term has been tuned as

=min(0.25, 0.1+0.075*d) for the winter 2016-2017.

II.2 The Model:

TOPAZ4 uses version 2.2.18 of HYCOM. In our implementation of HYCOM, the vertical

coordinate is isopycnal in the stratified open ocean and z-coordinates in the unstratified

surface mixed layer. Isopycnal layers permit high resolution in areas of strong density

gradients and better conservation of tracers and potential vorticity; and z-layers are well suited

to regions where surface mixing is important. To realistically simulate the circulation in the

Arctic region, an ocean model requires a particularly accurate representation of the dense

overflow and the surface mixed layer to isolate the warm Atlantic inflow from the sea ice. In

our opinion this makes HYCOM a suitable model for the North Atlantic and Arctic region

that spans the stratified open ocean, a wide continental shelf, regions of steep topography, and

extensive sea ice. HYCOM also permits sigma coordinates that can be beneficial in coastal

regions, however we have not adopted this option here.

Compared to TOPAZ3 [Bertino and Lisæter, 2008], the model has been modified for

simulating better the different water masses in the Arctic. Improvements include higher

vertical resolution to improve the inflow of Atlantic water, fine tuning of the model

parameters for viscosity and diffusion, and reduction of relaxation artefacts. Also, improved

river run-off and the inclusion of transport through the Bering Strait improve the inflow of

fresh water into the Arctic.

The TOPAZ4 implementation of HYCOM uses: the tracer and continuity equation solved

http://www.meereisportal.de/


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with the second order flux corrected transport [FCT2, Iskandarani et al., 2005; Zalesak,

1979]; the turbulent mixing sub-model from the Goddard Institute for Space Studies [Canuto

et al., 2002]; the vertical remapping for fixed and non-isopycnal coordinate layers with the

Weighted Essentially Non-Oscillatory (WENO) piecewise parabolic scheme; the short wave

radiation penetration with varying exponential decay depending on the Jerlov water type

[Halliwell, 2004]; and biharmonic viscosity.

The model is coupled to a one thickness category sea ice model with elastic-viscous-plastic

(EVP) rheology [Hunke and Dukowicz, 1997]; its thermodynamics are described in Drange et

al. [1996] with a correction of heat fluxes for sub- grid scale ice thickness heterogeneities

following Fichefet and Morales Maqueda [1997]. The sea ice strength is set to 27500 N.m−2.

The advection of ice concentration, ice thickness, snow depth, first year ice fraction and ice

age is calculated using a 3rd

order WENO scheme [Jiang and Shu, 1996], with a 2nd

order

Runge-Kutta time discretisation.

The model domain covers the North Atlantic and Arctic basins (see Figure 2), with the

horizontal model grid created by a conformal mapping with the poles shifted to the opposite

side of the globe to achieve a quasi-homogeneous grid size [Bentsen et al., 1999]. The grid

has 880 × 800 horizontal grid points, with approximately 12-16 km grid spacing in the whole

domain. This is eddy-permitting resolution for low and middle latitudes, but is too coarse to

properly resolve all of the mesoscale variability in the Arctic, where the Rossby radius is as

small as 1-2 km.

The model uses 28 hybrid layers with carefully chosen reference potential densities of 0.1,

0.2, 0.3, 0.4, 0.5, 24.05, 24.96, 25.68, 26.05, 26.30, 26.60, 26.83, 27.03, 27.20, 27.33, 27.46,

27.55, 27.66, 27.74, 27.82, 27.90, 27.97, 28.01, 28.04, 28.07, 28.09, 28.11, 28.13 1. The top

five target densities are purposely low to force them to remain z-coordinates. The minimum z-

level thickness of the top layer is 3 m, while the maximum z-layer thickness is 450 m, to

resolve the deep mixed layer in the Sub-Polar Gyre and Nordic Seas. The model bathymetry

is interpolated from the General Bathymetric Chart of the Oceans database (GEBCO) at 1-

minute resolution.

The model is initialized in 1973 using climatology that combines the World Atlas of 2005

[WOA05, Locarnini et al., 2006; Antonov et al., 2006] with version 3.0 of the Polar Science

Center Hydrographic Climatology [PHC, Steele et al., 2001]. At the lateral boundaries, model

fields are relaxed towards the same monthly climatology. The model includes an additional

barotropic inflow through the Bering Strait, representing the inflow of Pacific water, with

seasonal variations. The inflow varies seasonally as found in observations (Woodgate et al.,

2005): with a maximum in June (1.3 Sv), a minimum in January (0.4 Sv), and the mean

transport is 0.8 Sv. [Ness et al., 2010]. This inflow is balanced by an outflow at the southern

boundary of the domain in the Atlantic Ocean.

For the reanalysis experiment presented in this paper, TOPAZ is forced at the ocean surface

with fluxes derived from 6-hourly atmospheric fluxes from ERA- interim [Simmons et al.,

2007] that has a resolution of 0.25◦. The atmospheric fields from ERA-interim include:

precipitation, dew point temperature, total cloud cover, air temperature at 2 m, sea level

pressure, wind speed at 10 m and long wave radiation at the sea surface. The incoming short

wave radiation is computed every 3h from synoptic cloud fields, and the wind stress is

derived from 10 m winds, estimated as in Large and Pond [1981]. The surface fluxes are


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forced with a bulk formula parametrisation [Kara, 2000].

The value of river discharge is poorly known because the observation array for river flows is

sparse. A monthly climatological discharge is estimated by applying the run-off estimates

from ERA-interim to the Total Runoff Integrating Pathways [TRIP, Oki and Sud, 1998] over

the 20-year reanalysis period (1989-2009). Rivers in HYCOM are treated as a negative

salinity flux with an additional mass exchange. The remaining inaccuracies in the evaporation

and run-off are constrained using relaxation towards climatology. However, relaxation can

have a detrimental impact on some regions – particularly where strong fronts occur and/or

they are misplaced (e.g., Gulf Stream). In such places the water mass distribution is bimodal,

and the relaxation towards an average estimate reduces the sharpness of fronts. To avoid this

problem, relaxation is only activated when the difference between the climatology and the

model is less than 0.5 PSU (Mats Bentsen, BCCR, pers. comm.).

The diagnosed model SSH is the steric height anomaly that varies due to barotropic pressure

mode, deviations in temperature and salinity, and does not include the inverse barometer

effect (atmospheric effect). The model mean SSH is computed over the period 1993-1999 and

used to assimilate altimeter observations (See Figure 3).

The model code is publicly available. It can be accessed from

https://svn.nersc.no/repos/hycom or browsed at https://svn.nersc.no/hycom/browser.

II.3 Data assimilation

TOPAZ4 has transitioned from using the traditional “perturbed observations” EnKF scheme

[Burgers et al., 1998] to the “deterministic EnKF”, or DEnKF, that was developed by Sakov

and Oke [2008a]. In the case of “weak” DA, when the increments are much smaller than the

ensemble spread, the DenKF is asymptotically equivalent to the symmetric right multiplied

ensemble square root filter (ESRF) [Sakov and Oke, 2008b], commonly known as the ETKF

[Bishop et al., 2001]. In the case of “strong” DA the DEnKF yields smaller increments than

the ESRF – a characteristic that can be interpreted as adaptive inflation, aimed at increasing

the robustness of the system.

Similar to TOPAZ3, TOPAZ4 uses a simple, non-adaptive, distance-based localization

method known as “local analysis” [Evensen, 2003; Sakov and Bertino, 2011]. With this

method, a local analysis is computed for one horizontal grid point at a time, using

observations from a spatial window around it. In contrast to TOPAZ3, TOPAZ4 uses smooth

localization (rather than a box-car type localization) that yields spatially continuous analyses.

The smoothing is implemented by multiplying local ensemble anomalies, or perturbations, by

a quasi-Gaussian, isotropic, distance dependent localization function [Gaspari and Cohn,

1999]. The localization radius, beyond which the ensemble-based covariance between two

points is artificially reduced to zero, is uniform in space and is set to 300 km. This

corresponds to an e1/2-folding radius of about 90 km.

During each analysis step, TOPAZ calculates a 100×100 local ensemble transform matrix

(ETM, called X5 in Evensen 2003) for each of the 880 × 800 horizontal model grid cells. The

matrix inversion involved in the calculation of each local ETM is performed either in

ensemble or observation space (whichever is smaller), depending on whether the number of

locally assimilated observations is greater or smaller than the ensemble size. This 880 × 800

array of ETMs is then used for updating each horizontal model field (about 150 fields total).

https://svn.nersc.no/hycom/browser


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The analysis is performed in the model grid space. The instances of negative layer thickness

or ice concentration, should they occur, are corrected in a post-processing procedure. The next

cycle is restarted from the analysis in a straightforward manner; without using incremental

update or nudging.

The DA code is publicly available. It can be accessed from https://svn.nersc.no/repos/enkf or

browsed at https://svn.nersc.no/enkf/browser.

II.4 History of the reanalysis

The initial ensemble is generated so that it contains variability both in the interior of the ocean

and at surface. The initial ensemble is generated from 20 model snapshots taken from a long

model run at a similar time of the year. Each of these states is used to produce five initial

states by perturbing the layer and ice thickness by 10% with the decorrelation length scale of

50 km. The perturbation of layer thickness also has vertical decorrelation distance of three

layers. The initial ensemble is integrated for 40 days to damp instabilities that result from the

perturbations.

After generating the initial ensemble the DA system is spun up during a period of 1 year, for

the calendar year of 1990. In order to limit the impact from an abrupt start of DA, the

observation error variance is at first purposely overestimated and gradually decreased to the

realistic level over a period of one year, starting from a factor of 8 and reducing to 1 at the end

of the year 1990 for an official start of the reanalysis in 1991.

The assimilation cycle is weekly, similarly to the real-time system, but more observations are

assimilated in delayed mode.

Table 4: Changes in the TOPAZ Reanalysis system (continues in next page).

Date Change Type

1. 07.10.1992 Start of assimilation of altimetry data Observation

2. ??.10.1993 Correction of wrong bias estimation for SST under ice Assimilation

3. 14.01.1998 Start of assimilation of Argo profiles Observation

4. 24.06.1998 Start of assimilation of OSTIA reanalysis, replacing Reynolds SST

Observation

5. 24.11.1999 Reduced variance of perturbations of air temperature to 2.25 K² Increased standard deviation of cloudiness and precipitation pertubations

Assimilation

6. 04.04.2001 Limit SST bias to 5 K Correction of defect in asynchronous assimilation Restoration of previous settings for pertubations of air temperature, cloudiness and precipitation

Assimilation

7. 26.09.2001 Limit SST bias to 10 K Assimilation

https://svn.nersc.no/enkf/browser


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Table 4: (continued) Changes in the TOPAZ Reanalysis system.

Date Change Type

8. ??.12.2001 Change of precipitation variance to 1.10-12 Assimilation

9. ??.05.2006 Change of bias estimation routine from an inflation approach to an AR1 process

Assimilation

10. 01.01.2008 Start of assimilation of OSTIA NRT product, replacing OSTIA reanalysis Start of assimilation of AMSR-E, replacing OSI SAF (no repro)

Observation

11. 01.01.2009 Start of assimilation of OSI SAF (repro), replacing AMSR-E Observation

12. 01.01.2010 Start of assimilation of OSI SAF (operational), replacing OSI SAF (repro)

Observation

13. 01.01.2011 Correction of a subsurface T/S bias in the central Arctic by relaxation, removal of the multiplicative inflation

Assimilation

14 05.02.2014 Correction of observation error of the OSTIA SST not accounted for in reanalysis.

Observation

15 01.01.2014 Assimilation of sea ice thickness from CS2SMOS in the winter month.

Assimilation

16 01.01.2018 Broader influence localization for in situ profiles. Reduced cutoff for assimilation of salinity profiles.

Assimilation

The above table lists changes in the TOPAZ Reanalysis system between 1991 and 2018. The given dates are the model dates of the days when the changes were introduced. The type of the changes indicates whether the changes occurred in the assimilated observations (e.g. new data set) or in the assimilation system.


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III VALIDATION FRAMEWORK

The validation uses the whole reanalysis. Some diagnostics (Degrees of Freedom of Signal,

DFS; and Spread Reduction Factor, SRF) will be illustrated on a typical day only although

they were monitored routinely. In a local application of the DEnKF, the DFS are calculated

locally as such: trace(KH), K being the local Kalman Gain matrix and H the local observation

operator. In the following, H is restricted to each observation type. The DFS is sensitive to

degrees of freedom that may explain little variance. Alternatively, the SRF is proposed as

follows

with P

f and P

a being respectively the forecast and analysis error covariance matrices, so that

the vectors explaining little variance would not count in the diagnostic. The SRF is also not

bounded by the total number of degrees of freedom. More explanations are given in Sakov et

al. (2012).

The performance is assessed in two ways:

- Stability check of the method: using assimilated data we verify that the filter error

statistics are in line with the actual errors and temporally stable.

- Oceanographic validation against a variety of (mostly) independent ocean and sea ice

observations.

Figure 1: Example of difference of TOPAZ4 SST in the Gulf Stream between an ensemble mean (left) and one individual member (right). Note the smoothing induced by ensemble averaging.


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A word of caution: The ARC MFC produces ensemble means as best estimates (100 members average) and as

forecasts (10 members average). Ensemble means are stored on a daily basis as they (in an

ideal Gaussian world) represent the best estimation in terms of accuracy. However, the

ensemble averaging smoothes out some of the finer structures (like additional diffusion),

which will affect the non-linear statistics (for example MLD, eddy kinetic energy, positive

and negative water transport …). This means that in principle all consistency type of

validation should be carried out using individual ensemble members, while accuracy type of

validation should use the ensemble average. However the 100 individual members of the

ensemble have not been stored at daily frequency to limit the storage costs. This should be

kept in mind when assessing the class 1-2-3 metrics. The difference between the ensemble

average and individual members can be seen on Figure 1. This means that all Class 1,2,3

metrics will be smoother than if any member had been picked from the ensemble instead of

the ensemble average. The effect should however not be too dramatic as can be judged from

Figure 1.

- Class 1 metrics are 2D maps at the surface and selected depths (out of the 12 depths

selected for NetCDF files), based on monthly averages and ensemble averages (best

guess). Polar Stereographic projection. Horizontal resolution is 12.5 km at the North

Pole.

- Class 4 are summaries of assimilation diagnostics (innovation statistics, ensemble

spread, bias) computed at each weekly cycle (thus instantaneous statistics, so that the

variability is larger than in the monthly averaged products) and averaged in boxes and

in different classes of depths in the case of in-situ profiles. The two boxes considered

are

o Arctic box: lat > 70°N, all longitudes

o Nordic box: 63°N < lat < 80°N, 30°W < lon < 20°E

Figure 2: Model depths and 63°N parallel used for Class4 statistics.


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Table 5: Metrics and status of calibration activity (continues overleaf).

Name Ocean

parameter

Status Supporting reference dataset

T-M-CLASS1-CLIM-MEAN temperature until 2010 only Climatology WOA13

SST-M-CLASS1-MEAN_XY temperature From 1999 SST_GLO_SST_L4_REP_OBSERVATI

ONS_010_011

SST-M-CLASS1-

LR_SLOPE_T-Arctic

temperature 1999-2014 SST_GLO_SST_L4_REP_OBSERVATI

ONS_010_011

SST-W-ASSIM-MEAN-

NordicBox

SST-W-ASSIM-STD-NordicBox

SST-W-ASSIM-RMS-

NordicBox

SST-W-ASSIM-AVAIL-NordicBox

SST-W-ASSIM-ENS_STD-

NordicBox

SST-W-ASSIM-SRF

SST-W-ASSIM-DSF

temperature Updated 2014 SST_GLO_SST_L4_REP_OBSERVATI

ONS_010_011 (pre-2007)

SST_GLO_SST_L4_NRT_OBSERVATI

ONS_010_005 (post 2007)

T-50m-D-CLASS2-MOOR-HOV

T-250m-D-CLASS2-MOOR-HOV

temperature from 1998 to 2007 AWI moorings

QNET-CLASS1-MOD-MEAN_T-2000-2010

QNET-CLASS1-MOD-BIAS-2000-2010

QNET-M-CLASS1-MOD-

MEAN_XY-2000-2010

SST-M-CLASS1-MEAN-XY-2000-2010

Temperature

and net

downward

fluxes

until 2010 only ASR (Bromwich et al. 2010)

T-0_100m-W-ASSIM-PROF-MEAN-NordicBox

T-0_100m-W-ASSIM-PROF-STD-NordicBox

T-0_100m-W-ASSIM-

PROF-RMS-NordicBox

T-0_100m-W-ASSIM-PROF-AVAIL-NordicBox

T-0_100m-W-ASSIM-

PROF-ENS_STD-NordicBox

T-0_100m-W-ASSIM-PROF-SRF

T-0_100m-W-ASSIM-

PROF-DFS

Also for depth 100_300m,

300_800m, 800_2000m and

boxed Atlantic and Arctic

temperature Updated 2014 INSITU_ARC_TS_REP_OBSERVATIO

NS_013_037


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Table 5: (continued) Metrics and status of calibration activity.

Name Ocean

parameter


S-0_100m-W-ASSIM-PROF-MEAN-NordicBox

S-0_100m-W-ASSIM-

PROF-STD-NordicBox

S-0_100m-W-ASSIM-PROF-RMS-NordicBox

S-0_100m-W-ASSIM-

PROF-AVAIL-NordicBox

S-0_100m-W-ASSIM-PROF-ENS_STD-NordicBox

S-0_100m-W-ASSIM-

PROF-SRF

S-0_100m-W-ASSIM-PROF-DFS

Also for depth 100_300m,

300_800m, 800_2000m and

boxed Atlantic and Arctic

Salinity Updated 2014 INSITU_ARC_TS_REP_OBSERVATIO

NS_013_037

MLD-CLASS1-CLIM-

MEAN_T-2002-2014-Nordic

Mixed layer

thickness

Over the 2002-2014

period

WOA2013 climatologie, NODC

WOA2013 data, Nordic Atlas (2002-

2012), NOAA MIMOC monthly

climatology

UV-15m-CLASS1-MEAN_T-

1993-2013

Currents Until 2013 Not available under ice

SL-M-CLASS4-ALT-

LR_SLOPE_T-Arctic

Sea level 1993-2013 SEALEVEL_GLO_SLA_L3_REP_OBS

ERVATIONS_008_018

SL-M-CLASS2-TG-CORR

SL-M-CLASS2-TG-EXPVAR

SL-M-CLASS2-TG-RMSD

SL-M-CLASS2-TG-

MEAN_XY-<region>

4 Arctic regions.

Sea level Until 2013 only Holgate et al. (2013) PSML

SLA-M-CLASS1-MOD_GLO-

MEAN_XY

Sea level 1993-2010 SEALEVEL_GLO_PHY_L4_REP_OBS

ERVATIONS_008_047

SLA-W-ASSIM-ALT-MEAN-NordicBox

SLA-W-ASSIM-ALT-STD-

NordicBox

SLA-W-ASSIM-ALT-RMS-NordicBox

SLA-W-ASSIM-ALT-

AVAIL-NordicBox

SLA-W-ASSIM-ALT-ENS_STD-NordicBox

SLA-W-ASSIM-ALT-SRF

SLA-W-ASSIM-ALT-DFS

Sea level Updated 2014 SEALEVEL_GLO_SLA_L3_REP_OBS

ERVATIONS_008_018


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Table 5: (continued) Metrics and status of calibration activity.

Name Ocean

parameter


TP_UV-0_600m-M-CLASS3-LIT-MEAN_T

TP_UV-0_600m-M-

CLASS3-LIT-STD_T

For 4 sections and Atlantic Waters

Volume

transport

until 2010 only Literature (Lien et al. 2016)

SIA-Y-CLASS3-SAT-MEAN_XY

SIA-M-CLASS3-SAT-MIN

Sea ice

concentrations

New SEAICE_GLO_SEAICE_L4_REP_OBS

ERVATIONS_011_009

SID-D-CLASS4-INS-RMSD

SID-D-CLASS4-INS-CORR

SID-D-CLASS4-INS-BIAS

SITH-D-CLASS4-INS-

RMSD

SITH-D-CLASS4-INS-CORR

SITH-D-CLASS4-INS-BIAS

SITH-CLASS1-SAT-

MEAN_T

SIV-M-CLASS1-SAT-

MEAN_XY

Sea ice draft /

thickness /

volume

New In-situ IceBridge and USSUB data

SIC-W-ASSIM-ALT-MEAN-ArcticBox

SIC-W-ASSIM-ALT-STD-

ArcticBox

SIC-W-ASSIM-ALT-RMS-

ArcticBox

SIC-W-ASSIM-ALT-

AVAIL-ArcticBox

SIC-W-ASSIM-ALT-

ENS_STD-ArcticBox

SIC-W-ASSIM-ALT-SRF

SIC-W-ASSIM-ALT-DFS

Sea ice

concentrations

Updated until 2014 SEAICE_GLO_SEAICE_L4_REP_OBS

ERVATIONS_011_009

SIUV-D-CLASS4-BUOY-MEAN_T

SIUV-D-CLASS4-BUOY-

BIAS_T

SIUV-D-CLASS4-BUOY-VAAD_T

SISPD-D-CLASS4-BUOY-

MEAN_XYT_month_Arctic

SISPD-D-CLASS4-BUOY-BIAS_XYT_month_Arctic

SISPD-D-CLASS4-BUOY-

RMSD_XYT_month_Arctic

Sea ice drift /

Sea ice drift

speed

New (1991-2011) SEAICE_ARC_SEAICE_L3_REP_OBS

ERVATIONS_011_010

In situ IABP buoys.


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IV VALIDATION RESULTS

IV.1 Assimilation diagnostics

IV.1.1 Sea level anomalies

At every cycle, the classical first and second order statistics are collected for each observation

assimilated and averaged on the two boxes defined above (Arctic and Nordic boxes). As the

altimeter data are masked by sea-ice the along-track sea level anomalies (TSLA) statistics are

given in the Nordic Seas box only (Figure 3). The number of observations after superobing

(#obs) remains relatively stable until the start of ERS2 in January 2000. The ensemble spread

remains almost constant all through the reanalysis, with vague indications of a seasonal signal

with larger expected errors in the winter. The ensemble spread is a factor of 10 inferior to the

actual errors from the innovations which is the way the EnKF indicates that the model

resolution is still too coarse for a quantitative representation of eddies in the Nordic Seas.

However, the combination of observation and ensemble errors fits quite well with the

innovation statistics, which indicates that the assimilation system is in good health for the

region (i.e., not over-assimilating). The expected EnKF error diminishes visibly with the

introduction of ERS2 in January 2000 and again in December 2001 with Jason-1.

The mean of innovations (bias) is surprisingly not showing any seasonal cycle, contrarily to

the Pilot Reanalysis, which confirms that the introduction of the bias estimation is efficient.

The total RMS of innovations (rms, bias not removed) decreases slightly along the reanalysis,

converging at a level of 5 cm (instead of 6 cm for the Pilot Reanalysis) and slightly higher in

winter than in summer as predicted by the ensemble spread. This can be attributed to the

unresolved mesoscale activity and high-frequency barotropic signals generated by the passing

winter storms.

Overall, the full reanalysis is performing better than the previous product – the Pilot

reanalysis - did over the period 2003-2008 with 3 flying altimeters instead of 2.


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Figure 3: Time series of TSLA innovations in the Arctic, note that the Kalman filter does not expect the red line to fit with the green line, but the difference is the observation errors (2-3 cm). The lines are smoothed by 28 days moving window. The peaks are marking the “resets” of the online bias estimation but do not affect the product fields themselves.

Figure 4: Time series of TSLA ensemble standard deviations in North Atlantic (Box 2) (ens, innovation

bias – observation minus model - and RMS, total innovation error tot and number of observations

#obs, note that the Kalman Filter aims at the coincidence of tot with the rms curves). Unit: meter.


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The SRF example in Figure 5 show that the assimilation of along-track SLA data is most

efficient along the satellite tracks, and in particular near the Gulf Stream and instability

regions. These are places where the ensemble spread is largest, due to the intense mesoscale

activity. The assimilation is inefficient in the Equatorial band, which is expected due to the

vanishing Coriolis force. There is therefore no specific treatment needed at the Equator. The

DFS are mostly in the range from 1-10, and the SRF is mostly below 1, which means that the

SLA data are not over-assimilated. Note that the tracks are denser in the North, justifying the

use of superobing.

Figure 5: SRF of along-track SLA on a random day (13th Dec. 2000)

The area-weighted mean sea level anomaly of the TOPAZ reanalysis and a free run is

compared to the SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047 product.

This dataset provides fields of absolute height above geoid on a domain between 82°S and

82°N with a regular grid spacing of 0.25 deg for the period from 1993 until present. These

fields result from a multiple linear regression of satellite data, which were partly assimilated

into the TOPAZ reanalysis as well.

The sea surface height fields of the TOPAZ reanalysis and the free run have been interpolated

onto the regular grid of the reference product. In order to make all datasets comparable,

anomalies from the respective long-term mean of the period from 1993 to 2010 have been

computed for both TOPAZ model runs as well as the reference dataset. For the latter, weekly

anomalies have been calculated first which have then been averaged for each month. Only

ice-free grid points were considered.


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Figure 6 shows the time series of sea level anomalies from 1993 to 2010 for the TOPAZ

reanalysis, the free run as well as the GLOBAL-REP PHYS 001-013. Both model runs

capture the seasonal variation well. However, their amplitude is mostly smaller than in the

reference dataset. Neither of the TOPAZ runs seems to perform significantly better which can

also be seen from there RMSD which is 2:1 cm for both runs. The correlation between the

reanalysis and the reference dataset is 0.87 and thus slightly lower than the one of the free run

and the reference which is 0.89.

Figure 6: Time series of the area-weighted domain average sea level anomalies from 1993 to 2013 for the TOPAZ reanalysis, the free run as well as the GLOBAL-REP PHYS 001-013.

Variations in sea surface height are directly linked and subject to all dynamic and

thermodynamic processes in the climate system (Proshutinsky et al., 2007). Thus, changes in

the components of the climate system can lead to a trend in sea surface height. This trend is

mainly attributed to:

changes of the water properties that can be divided into thermosteric (largest) and

halosteric parts,

land uplift that neutralises when averaged over the global averaged but is regionally

non-zero

melting of land ice, almost of the same magnitude

changes in storm tracks, which may lead changes of the pressure

In the TOPAZ system, only the steric influence of sea level rise is accounted for. According

to the latest assessment report of Intergovernmental Panel on Climate Change, the rate of

global mean sea level rise due to thermal expansion was 1:1 mm/yr between 1993 and 2010

(Church et al., 2013, Table 13.1, p. 1151).

Monthly anomalies from the mean annual cycle of sea surface height at each grid point in the

TOPAZ reanalysis and a free run have been computed. The linear trend has been estimated

with a least-squares fit to the anomalies for the period from 1993 to 2013. During this period,

along-track sea level anomalies from satellite altimeters were assimilated into the reanalysis

system.


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In Figure 7, the sea level trends at each grid point of both model runs are displayed. Overall,

both free run and reanalysis show a sea level rise in most regions of the Arctic. However,

regions with a negative trend can be found in the Baffin Bay and in the Norwegian Sea. While

both the reanalysis and the free run show a positive trend in the Beaufort Sea, the picture is

different in the central Arctic around the North Pole. Here, the reanalysis shows an increase in

sea level while the free run shows a negative trend. North of Greenland, the opposite is the

case. Furthermore, the reanalysis shows a negative trend in the Greenland Sea, which is much

less pronounced in the free run. We can attribute a large part of the differences to the changes

of spatial distribution of ice cover and its impact on the momentum fluxes to the ocean.

Despite the spatial differences in the trends of the reanalysis and the free run, the average sea

level rise over the whole Arctic Domain is 2 mm/yr in both model runs.

Figure 7: Sea level trend maps.

IV.1.2 Sea surface temperatures

Similarly to the SLA data, the SST are masked below the sea-ice so that the validation in the

Central Arctic is not possible. We thus show here only the results in the Nordic Seas box. The

SST assimilated transition from coarse resolution Reynolds SST to high-res OSTIA SST in

June 1998. This is visible by the jump of #obs from below 500 to about 7000 observations per

cycle in the Nordic Seas box. The number of observations is slightly larger in summer than

winter due to the sea ice coverage in the Greenland Sea. From the date of the switch and after

an initial spike caused by the different land mask in high resolution, the RMS differences

diminish gradually and stabilize at about half of the error one year later, showing that there is

a long-term benefit in assimilating high-resolution SST, the innovation bias also lowers by


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about half a degree, indicating a difference between the NOAA and the OSTIA products. The

ensemble spread in SST slightly lower than the innovation statistics but the underestimation is

much less dramatic than it was in the Pilot Reanalysis. After the switch to high-res SST, the

ensemble is actually overestimating the error, which would indicate that the OSTIA

observation errors are overestimated. The ensemble spread remained stable, but also shows

seasonal variations with a higher spread in the short Nordic summer, which can be explained

by the development of the mixed layer, where the SST is more sensitive to the atmosphere.

The SST bias shows also a clear seasonal signal of amplitude 1 deg C, half of what it was

during the Pilot Reanalysis. The SST bias is more pronounced in summer than winter because

of the higher SST sensitivity in the thinner summer mixed layer where the model is too warm.

One possible explanation could be the formulation of the wind drag coefficient in Drange and

Simonsen (1996), which does not account for air-sea gradients of temperature and humidity,

the latter being important in the Nordic Seas. The “warm SST bias” is consistent with other

HYCOM studies (Winter and Evensen, 2007, Wallcraft, presentation at the LOM meeting),

the default option in the HYCOM model is to apply a constant offset of the heat fluxes to

counteract, but this was not applied in TOPAZ4. The mean innovation does not show any

linear trend, which is a reassuring evidence that the reanalysis system does not produce its

own warming or cooling, but the peaks of summer bias increased after switching to OSTIA,

which motivated a new bias estimation procedure that would be robust to changes of data

sources. This has been carried out in 2006 and the peaks of bias did indeed reduce in the

following years.

Figure 8: Same as Figure 3 for SST.


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Figure 9: Same as Figure 3 for SST in Box 2 (North Atlantic). Unit: deg C. Note that the tot

overestimates the rms after the transition from Reynolds to OSTIA in 1998. The larger ensemble STD deviation between 2011 and 2014 is due to higher observation errors the OSTIA product, this incident has been corrected before it could affect the bias and rms scores.

The SRF for the assimilation of HR OSTIA SST are overall higher than those of SLA, but the

same regions pop out for the higher sensitivity to observations: the retroflection of the

Equatorial Current North of Brazil, the Gulf Stream and the vicinity of the ice edge.

Following the fifth assessment report of Intergovernmental Panel on Climate Change

(Hartmann et al., 2013, p. 192), it is certain that global average sea surface temperatures have

increased since the beginning of the 20th century". Especially in the Arctic a strong increase

of the sea surface temperature is apparent (Zhang, 2005). In order to be useful for

climatological studies, an ocean reanalysis should be able to capture this trend.

The sea surface temperature trends of the ARC MFC reanalysis and a free run have been

compared to the assimilated OSTIA data. As described above for the long-term mean of the

sea surface temperature, a combination of the OSTIA reanalysis and the near-real time

OSTIA interpolated onto the model grid has been used. In opposition to above, the provided

sea ice mask has been used to set all values under ice to the average melting point of -1.8 C.

This is the same approach that was used when the OSTIA data were assimilated into the

reanalysis from June 1998 onwards. In order to avoid artificial trends caused by the different

assimilated sea surface temperature datasets and to account for the spin-off after their

exchange, the period from 1999 to 2010 is considered only. The linear trend has been

estimated with least-squares fit to the anomalies from a mean annual cycle of the considered

period.


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Figure 10: SRF of SSTs on 13th Dec. 2000 (OSTIA SST).

In Figure 11, the sea surface temperature trends at each grid point of different datasets are

displayed. The top-left panel shows the trend of the raw reanalysis while top-right panel

depicts the trend of the reanalysis when the estimated bias of the sea surface temperature has

been subtracted instantaneously. Both maps show a similar pattern of a mostly positive trend

outside the mainly ice-covered region around the pole. Only east of Iceland and along the

Norwegian coast a slightly negative trend is visible.

Except for the regions with a negative trend, the overall picture agrees with the trend of the

OSTIA dataset shown as a reference in the lower-right panel. The large positive trend along

the Russian coast at the Barents Sea and Kara Sea should not be over-interpreted. These may

occur due to the interpolation of the OSTIA data onto the model grid and the different land-

sea masks in both datasets.

Looking at the sea surface temperature trend in the free run in the lower-left panel of Figure

11, differences to the reanalysis and the observational data are clearly visible. The area of a

positive trend in the Beaufort Sea is smaller in the free run because of the generally larger sea

ice extent. While both the reanalysis and the observations show a positive trend in the Davis

Strait, there is no significant trend visible in the free run. Furthermore, the free run exhibits a

negative trend in sea surface temperature along the coast of Lofoten and around North Cape.

These differences lead to a lower mean trend in the Arctic of 0:021 C/yr compared to 0:03

C/yr in OSTIA. For both the raw and the bias-corrected dataset of the ARC MFC reanalysis,

the trend is about the same as in the observations which should be expected since OSTIA was

assimilated.

In Figure 12, the upper panel shows the time series of the area-weighted Arctic mean sea

surface temperature for OSTIA (black), the instantaneously bias-corrected ARC MFC

reanalysis (red) and the free run (blue), respectively for the period from 1999 to 2010. The

vertical dashed line indicates the time of the transition from the OSTIA reanalysis to the near-


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real time OSTIA product. It can be seen from that figure that the seasonal cycle is captured

well in both the reanalysis and the free run. However, the maximum in late Summer is always

higher in the models, whereas the differences are generally larger for the free run. In

opposition, the winter minimum of the free run is mostly closer to the observations due to the

limited SST under sea-ice.

The time series of the anomalies from a mean seasonal cycle for the period from 1999 to 2010

for the Arctic region is shown with thin solid line in the lower panel of Figure 12.

Additionally, the linear trends estimated with a least-squares fit are shown as thicker lines and

their respective slope is given in the legend. Note that the trend of the OSTIA dataset is

covered by the red line since both are almost the same with 0.032 C/yr in the reanalysis and

0.03 C/yr in OSTIA, respectively. In general, the anomalies of the ARC MFC reanalysis are

closer to the observed anomalies which results in a smaller trend of 0.021 C/yr in the free run.

From both panels in Figure 12, there is no significant difference between the two OSTIA

datasets and neither does it seem to have an effect in the data assimilation.

Figure 11: Maps of SST trends. The bias correction corresponds to the online bias estimation procedure using the EnKF.


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Figure 12: Time series of SST trends in the Arctic. Reanalysis compared to observations and model free run. Note that the OSTIA and TOPAZ reanalysis are in phase from 1998 to 2005, then TOPAZ assimilated the operational SST product, so the agreement may look poorer due to the difference of SST processing.

IV.1.3 Sea ice concentrations

The Arctic box is considered for statistics of sea ice concentrations (Figure 13). The number

of observations reflects the ice area because of the masking of data in the open ocean. The

ensemble spread follows a seasonal cycle with larger spread in summer, consistently with the

initial study from Lisæter et al. (2003). The innovation errors follow a similar seasonal cycle

but not long-term trend appears in the rms differences although the ensemble spread doubles

after the increase of model errors in cloudiness and precipitation in 2000. The rms differences

are stable at about 10% of concentrations, which is a one-third reduction compared to the

MyOcean reanalysis (Sakov et al., 2012). The mean bias is negative during the winter since

the OSI-SAF data never exceed 95% in pack ice, while the model simulates 99.5% ice

concentrations there, but changes depending on the sea ice algorithm used (NORSEX uses


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different tie-points than the OSI-SAF algorithms). A positive peak is also visible during the

summer as a sign that the summer ice is still underestimated in the model. These biases are

overall smaller than during the V1 Pilot Reanalysis.

Figure 13: Same as Figure 3 for ice concentrations. The vertical line marks the introduction of adaptive observation pre-screening and the increase of model errors. Unit: Dimensionless ice fraction. The discontinuities are caused by changes of input data using different algorithms (OSI-SAF, AMSR-E with NORSEX etc.) The ice concentration is not visibly affected by the assimilation of ice thickness after 2014.

The SRF diagnostics is presented in Figure 15, and show that the sensitivity is mostly located

at the ice edge, with some occasional risks for over-fitting in some points (SRF above 2). In

the summer, the sensitivity appears into the ice pack (not shown).

The reanalysis represents well the minimal summer ice extent of the extreme years 2007 and

2012 (Figure 16).


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Figure 14: Yearly series of the sea ice extent averaged in each year in the regions of Pan-Arctic Ocean, Greenland Sea, and Barents Sea respectively.

Figure 15: SRF of ice concentrations on 13th Dec. 2000.


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Figure 16: Minimal September sea ice extent during the extreme years 2007 (left) and 2012 (right). The black dashed line is the sea ice extent from the OSI-TAC reprocessed product (15% isoline) and the pink line is from the ARC MFC reanalysis. The background colour is the reanalysis ice concentrations. Monthly averages for September are presented.

IV.1.4 Temperature profiles

The temperature profiles are mostly originating from research cruises in the Nordic Seas

assembled in the Nansen database. The number of observations is at best 400 in the Summer

(and nothing in the winter), which is only half of what will become available with Argo buoys

after the year 2007. Also note the drop of – mostly Russian - data occurring between 1990 and

1991. The ensemble spread is higher than the actual errors (around 1 degree), which also

indicates a good health of the system. They are stable all along the 10 years of reanalysis. The

errors and bias are twice smaller than they were in the Pilot Reanalysis and the bias remains

mostly within +/- 0.5 deg C, increasing after the switch from Reynolds to OSTIA in June

1998.

The upper layer exhibits a small seasonal bias of amplitude smaller to that of SST. The

ensemble spread also increases by a factor of 2 during summer.


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Figure 17: Same as Figure 3 for temperature profiles in the Nordic Seas box (top 100 meters). Note the reduction of errors during the IPY years 2007-2009.

Figure 18: Same as above between 100 and 300 m depths (Atlantic water layer).


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Figure 19: Same as above in the inferior Atlantic Water layer.

Figure 20: Same as above in the deepest observed layers (Arctic Waters).


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The SRF of temperature are shown in Figure 21. The numbers are as high as the SRF of altimeter data, but also higher in the Equatorial band, which indicates that the TOPAZ4 system takes benefit of the complementarity between satellite and in-situ data.

Figure 21: SRF of Temperature profiles. 13th Dec. 2000.

IV.1.5 Salinity profiles

Similarly to the temperature profiles, there are no salinity profiles in the Arctic before the

IPY. The profiles taken by research cruises in the Nordic Seas vary in abundance from

nothing in the winter to 300 data points in the summer in the upper 30 m of water (See Figure

22). The year 1990 also had the most abundant observations until the deployment of Argo

floats in 2006. The error expected by the EnKF decreases in the spinup year 1990, then

remains relatively stable at 0.25 psu, which is most often above the actual RMS differences,

except during peak periods- usually during the Autumn - when a negative bias (model too

saline) occurs. One should note that the ensemble spread also increases during the peak

periods, which indicates that the model errors partially capture the process in cause.

Insufficient precipitation in the ECMWF data or the related upper water stratification may be

the cause of the saline biases in the Autumn, that season being is pretty rainy in the Nordic

Seas as you may know. Apart from these episodes, the surface salinity bias remains

remarkably close to zero during the 10 years of reanalysis.


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Figure 22: Spread and innovation statistics for salinity profiles in the upper 100 m Nordic Seas. Note the data are so few in the early years that the statistics are not stable.

Figure 23: Same as above, in the range of 100-300 m depths.


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Figure 24: Same as above in the range 300-800 m depths.

Figure 25: Same as above in the deepest measured waters.


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Figure 26: SRF of salinity profiles on 13th Dec. 2000.

IV.1.6 Relative impact of each data type

The overall DFS and SRF are presented in Figure 27. The central Arctic is tragically

unobserved compared to the rest of the Atlantic Ocean. Close to the ice edge, the sea ice

concentrations are the most effective data source and the SST and TSLA are most important

in the open ocean. Individual T/S profiles are also visible in the SRF map, showing that this

data source is as important as satellite data in the TOPAZ4 setup.

Figure 27: Total DFS and SRF of all observations on 13th Dec. 2000.


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IV.2 Validation against independent data

IV.2.1 Tide gauges

The sea level anomalies in the TOPAZ reanalysis and a free run have been compared to tide

gauge data (Holgate et al., 2013; Permanent Service for Mean Sea Level (PSMSL), 2014).

Monthly mean sea surface height time series of all observation sites north of 63N with at least

four years of measurements have been selected and considered as the reference for the

validation. For each station, the grid point closest to the position of the tide gauge has been

selected. Stations with a minimum distance to the next model grid point smaller than 40 km

have been rejected. Since the TOPAZ system and the tide gauge data use different reference

heights above which the sea surface height is measured, only anomalies from a station-

specific mean sea level for each dataset have been considered.

All observations that met the criteria above were assigned to four regions in order to analyse

them separately. The boundaries of the regions are shown in Figure 28. Within these regions,

both the observations and the model data of the TOPAZ reanalysis and a free run were

spatially averaged. In Figure 31, time series of the average sea level anomalies in the four

regions are shown. Both the TOPAZ reanalysis and the free run are very close to each other

and show about the same variability. Compared to the tide gauge measurements, the model

runs are in good agreement to the temporal evolution of the sea level anomalies. However, the

amplitude in the model is significantly smaller that the observations. A likely reason for this

behaviour is that the tide gauge data are point measurements whereas the sea surface height at

each model grid point represents an average sea level over an area of several square

kilometres.

Figure 28-30 show the correlation, explained variance and root-mean-square differences

(RMSD) respectively, for each individual station. Note that not all stations cover the full

period from 1991 to 2013. Generally, the models are in good agreement to the observations

and only at a few observation sites, noticeable differences can be seen between the TOPAZ

reanalysis and the free run. At these stations, the free run mostly shows a slightly better

performance than the reanalysis.

In all figures, regional differences are clearly visible in both model runs. Along the

Norwegian coast, the RMSD is very small in both the reanalysis and the free run. Also, the

correlation is very high and the model explains a large proportion of the observed variance in

this region. The highest RMSD values occur in both models in the Laptev Sea usually at

stations where the correlation is low. At some stations in this region, the error variance is

larger than the variance in the observations and thus the percentage of explained variance is

slightly negative. This seems to be the case more often in the reanalysis than in the free run.


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Figure 28: Correlation with observes sea level from tide gauges in The Arctic.

Figure 29: Explained variance for tide gauges.


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Figure 30: Errors, as given by the Root Mean Square Differences w.r.t tide gauges.


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Figure 31: Times series of tide gauges averaged in the boxes defined in the above plots.

IV.2.2 Current velocities

In order to investigate the near-surface circulation in the TOPAZ system, the model

climatology of currents at 15 m depth has been analysed. This is the typical depth of ocean

current measurements without direct influence of the wind (Niiler, 2001; Lumpkin and

Johnson, 2013). Figure 32 shows in colour the long-term mean velocity in 15 m depth of the

TOPAZ reanalysis and the free run for the period from 1993 to 2013. Additionally, the

vectors indicate the direction of the currents.

Both model runs capture the general Arctic near-surface circulation well. However, their

climatologies also show differences in strength and location which will be discussed in the

following. The current along the Arctic shelf break is well established. Especially in the Kara

Sea and the Laptev Sea in the reanalysis, it is well pronounced. Also, the Transpolar Drift

along the Lomonosov Ridge is more distinct in the reanalysis while it seems to be weaker, but

also dislocated towards the Siberian coast in the free run.


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Furthermore, the Beaufort Gyre in the reanalysis is only a narrow current whereas it is a

stronger, clearly anticyclonic feature in the free run. Since both models have the same lateral

boundary conditions, the current entering the Arctic Ocean through the Bering Strait is

similar. Also, the East Greenland Current is similar. On the other side of the Norwegian Sea,

the West Svalbard Current is well established in the free run, while it is almost not present in

the reanalysis.

For the Baffin Current, the models show a slightly different behaviour. In the reanalysis, it

flows southward aligned to the coast of Baffin Island whereas it reaches further into the

Baffin Bay in the free run. The Inflow into the Artic via the Fram Strait which is known to be

too weak, is clearly reinforced in the reanalysis. The circulation is also clearly improved in

the Norwegian Sea with a pronounced double branching off Svinøy and the circulation around

the Lofoten Bassin.

These mentioned differences between the reanalysis and the free run result from the

differences in the stratification of the Arctic Ocean and also from the differences in the mixed

layer depth of both models.

Figure 32: Time-averaged current velocities at 15 m depths from the ARC MFC reanalysis (left) and corresponding free run without assimilation (right).


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IV.2.3 Surface Heat Fluxes

At the sea surface, the interface between ocean and atmosphere, the net surface heat flux

which is downward positive describes the amount of energy that is released by the atmosphere

and taken up by the ocean. In the Arctic, the ocean releases heat into the atmosphere on a

long-term mean. Thus, the average net surface heat flux is negative over open water while it is

positive over ice. In this study, the net surface heat flux has been analysed and compared to

the Arctic System Reanalysis (ASR) (Bromwich et al., 2010).

The ASR is an atmospheric reanalysis for the Arctic region and is based on the Polar Weather

Research and Forecasting (WRF) model (Hines and Bromwich, 2008; Bromwich et al., 2009;

Hines et al., 2011). It provides among others fields of downwelling longwave and shortwave

radiation, latent and sensible heat flux as well as albedo, emissivity and skin temperature on a

polar stereographic grid with a grid spacing of 30 km for the period from 2000 to 2010. From

the mentioned variables, the net surface heat flux has been computed and the grid points over

land have been masked out.

In order to make the TOPAZ output comparable, data of the reanalysis as well as of the free

run were interpolated onto the grid of the ASR. Figure 33 shows the long-term mean net

surface heat flux for both the TOPAZ reanalysis and free-run. In regions which are generally

ice-free during the whole year (e. g. Norwegian Sea, Barents Sea), the average net surface

heat flux is mainly negative. Thus, there is a net release of energy from the ocean into the

atmosphere. Compared to the TOPAZ reanalysis, this heat release is smaller in the Norwegian

Sea and larger in the Davis Strait in the free run. In the ice-covered regions the fluxes are in

balance.

The differences between the long-term mean net surface heat flux of the TOPAZ reanalysis

and ASR as well as of free-run and ASR are shown in Figure 34. From this figure, it can be

seen that both the reanalysis and the free run are in good agreement to the ASR in the regions

around the central Arctic that are covered by sea ice most of the time. Along the ice edge,

large discrepancies occur due to the different sea ice representations.

Generally, the differences are smaller for the reanalysis compared to the free run.

Furthermore, the connection between the net surface heat flux and the sea surface temperature

(SST) becomes visible. At the west coast of Greenland in the Davis Strait, the heat transport

into the atmosphere is larger in the free run than in the reanalysis and also the SST in this

region is higher in the free run. Additionally, the larger differences of the free run in the

Norwegian Sea mean that less heat is released into the atmosphere compared to the reanalysis.

This is the same region where a cold bias in sea surface temperatures can be found. The

overall smaller differences of the TOPAZ reanalysis result in an RMSD of 21:23 W/m2

compared to the RMSD of the free run of 31:83 W/m2.

Figure 35 shows the time series of the monthly mean surface net heat flux in the upper panel.

In the panel below the monthly mean sea surface temperature is shown. Both variables have a

strong seasonal cycle with their maxima during the summer months which is captured well in

the TOPAZ system. Again, the connection between these variables is visible. There is a phase

shift between net surface heat flux and SST by about one month.

From the upper panel it can be seen that the amplitude of the heat flux is generally smaller for

both the TOPAZ reanalysis and FREE compared to the ASR. Among the TOPAZ model runs,

the reanalysis has a slightly larger amplitude and is thus closer to the reference dataset. This

larger variability in the net surface heat flux results in smaller maxima of SST during the

summer months which are closer to observations.


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Figure 33: Time-averaged downward heat fluxes into the ocean.

Figure 34: Differences of the above heat fluxes to the Arctic System Reanalysis (Polar WRF)


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Figure 35: Time series of downward heat fluxes (top) and SST (bottom) averaged on the Arctic.


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IV.2.4 Sea ice thickness and Ice volume

So far there is very little sea ice thickness data to provide a spatial description in a basin-scale,

and to be validated in an operation forecast system. The Unified Sea Ice Thickness Climate

Data Record (Lindsay, 2013) results from a concerted effort to collect as many observations

as possible of Arctic sea-ice draft, freeboard, and thickness. Used by the validation of the

TOPAZ reanalysis of 1991-2013, the related in situ observations include the sea ice draft by

Sonar of US Navy Submarines from National Snow and Ice Data Center (USSUB-DG and

USSUB-AN, Wensnahan and Rothrock, 2005; Rothrock and Wensnahan, 2007), and the sea

ice thickness by flight campaigns from NASA Operation IceBridge (IceBridge, Kurtz et al.,

2013), shown as in Fig. 36(a).

Relative to the sea ice thickness from the IceBridge covering the period of 2009-2011, the

bias of thickness from the reanalysis is thinner nearly to 1.1 m, and the RMSD is about 1.4 m.

Compared to the sea-ice draft data from USSUB-AN and USSUB-DG, the simulated sea ice

thickness from the reanalysis has been converted. Relative to the sea-ice draft, the bias from

the reanalysis is thinner about 0.34 to 0.37 m, and the RMSD is about 0.59 to 0.7 m. The

correlation coefficients are 0.86 and 0.69, which is significantly higher than that of the sea ice

thickness comparison.

Figure 36: Validation the sea ice thickness of the reanalysis by relevant in situ observations. (a) In situ positions in the Central Arctic; (b) compared to the sea ice thickness from IceBridge; (c) compared to the sea ice draft from USSUB-AN; (d) compared to the sea ice draft from USSUB-DG.

(a) (b)

(c) (d)


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Figure 37: Spatial pattern of mean sea ice thicknesses from TOPAZ (upper) and ICESat (middle), and their difference (bottom) for February-March (in left column) and October-November (in right column) during 2003-2008.


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To validate the uncertainties of sea ice thickness in spatial patterns, the satellite-derived

records from the Arctic Ocean ice cover from 10 Ice, Cloud, and land Elevation Satellites

(ICESat, Kwok et al., 2009) in 2003-2008 are used to assess the fidelity of the reanalysis

simulations for February-March and October-November respectively. Figure 37 shows the

spatial distributions of the mean sea ice thicknesses and their differences. Here, the sea ice

thickness from the reanalysis has been interpolated on avail part of the grid of ICESat.

Compared the two mean ice thicknesses, the spatial correlations are 0.74 and 0.87 for spring

and fall, respectively. On average, the difference of the mean ice thicknesses between of the

reanalysis and of the ICESat is about -0.79 m and -0.64 m, respectively. In spring, the

significant thinner bias of ice thickness appears at north of Ellesmere Island and nearly to -

2 m, and in fall where the thinner bias looks over -2 m.

The ice thickness data is very sparse in the Arctic but the estimates from Kwok et al. (2009)

do indicate that the reanalysis does underestimate the sea ice volume in all seasons, while the

free run had a tendency to overestimate it. New data sources (SMOSIce and CryoSAT) will

provide a better background for further tuning, which would be pointless with the data

presently at hand and the substantial amount of unknowns.

There are however points of satisfaction from the comparison, which are the indications of

reduced volume as expected from climate warming.

Figure 36: Comparison of ice volumes from the reanalysis and independent IceSAT data (Kwok et al., 2009 and Zygmuntowska et al., 2013). Right: yearly cycle of ice volume, mean and standard deviations over the whole reanalysis (e.g., Xie et al., 2017). This time series has not yet been updated after the assimilation of CS2SMOS data.

IV.2.5 Sea ice drift

The 3-days sea-ice drift trajectories from CERSAT (Ezraty et al., 2006) have been assimilated

into the reanalysis during the period 2002-2013. We use for validation the in situ buoy data of

the International Arctic Buoy Program (IABP) data set from 1991 to 2011 (from P. Rampal,

personal communication, 2014), averaged to daily frequency. The sea ice velocities simulated

by the reanalysis are interpolated into the buoy locations.


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Figure 39: Top: Sea ice drift vectors (arrows) and speeds (color shading) averaged over the period 1991-2011 from TOPAZ reanalysis (right) and IABP buoys (left). The center of the anticyclonic Beaufort Gyre marked with a magenta circle is located at (155°W, 78.1°N) in the reanalysis; and at (145°W, 77°N) in the observations. Bottom: (c) difference of the mean drift speeds and (d) absolute angular difference of the sea ice drifts

To avoid the “survival bias” caused by retreat of sea ice from the marginal seas and

unresolved coastal effects, the buoy drift vectors are limited to the central Arctic. The waters

deeper than 30 m and further than 50 km off the coastline are considered. To ensure the

around observation sample being enough, the coarse grid is defined on the TOPAZ model

grids with every 4 grids interval. Then through the spatial 9 grids smoothing in which the

averaging has evenly done all around grids, the mean drift fields of sea ice in 1991-2011 can

be shown in the top left panel of Figure 39. In the whole of Arctic basin, the two averaged

drift patterns are very similar with a clear anticyclonic gyre around the Beaufort Sea.

Focusing on these two mean sea ice drifts, the differences of both the speeds and the

directions are shown in the bottom panels of Figure 39. From the reanalysis, the mean drift

(a) (b)

(c) (d)


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speeds are mostly fast relative to the buoy, and the bias is about 1.7 km d-1

at all. However,

near the boundary areas including: the southern Beaufort Sea, to the Chukchi Sea, and to the

East Siberian Sea, the slower biases over -2 km d-1

are clear, and at there the drift velocities

from the reanalysis distribute rather wide and loosen. The obvious differences of drift

direction are emerged in the Canada Basin with more than 40 degree. Around (150°W, 77°N),

the two drift directions are almost convert, where it is related to the anticyclonic drift gyre in

the reanalysis close to the Beaufort Sea, and the anticyclonic center is too northwestward.

Figure 40: Top: Monthly time series of the daily averaged sea ice drift speeds in the Central Arctic from the TOPAZ reanalysis (blue line) and the IABP buoys (green line) during 1991-2011. The error bars represent the standard deviations of the daily estimates for each month. Bottom: Monthly time series of the bias (cross line) and RMSD (red line) of the sea ice drift speeds at the buoys locations.

Furthermore, the monthly series of the averaged drift speeds from the reanalysis and the buoy

during 1991-2011 are shown in the top panel of Figure 40. On average, the drift speed is

about 7 km d-1

for buoy data, and about 9.4 km d-1

for the reanalysis. The fast bias almost

exists all the time period except of 2011-2013. In the last 3 years, the drag coefficient for

atmosphere acting on sea ice had been reduced with the scale from 0.4 to 0.29. The month

series of the drift bias and RMSD in this period is shown in the bottom panel of Figure 40.

The bias in the long term is about 2.5 km d-1

, and consistent with the mean drifts comparison

in the top panel. The corresponding RMSD is about 5.1 km d-1

, and has an enlarged trend

after 2007 which indicates these two kinds of sea ice drift have an enlarged difference on

spatially. Rampal et al. (2008) found the sea ice drift in the central Arctic has a faster trend. In

this figure, the fast bias in the reanalysis also reduced after 2007.


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IV.2.6 Mixed layer thickness in Nordic Sea

The TOPAZ MLD show signatures of the inflowing Atlantic Water into the Norwegian Sea

(Mork and Skagseth, 2010), the Lofoten Vortex (Raj et al., 2015) and the return flow of

Atlantic Water into the Norwegian Basin (Read and Pollard, 1992) as seen in the

observations (see Fig. 41). However the deep mixed layer in the Greenland Basin is not

represented in the model. There are also inconsistencies among observations, for e.g., the

signature of the Lofoten Vortex is best represented in the Nordic Atlas, while the least in the

MIMOC climatology. The deep MLD found near the West Spitsbergen Current region in the

model is not seen in any of three observations used here. But it should be noted that a

previous study (Raj et al., 2015) using NISE data (Nilsen et al., 2008) has noticed this deep

MLD in the West Spitsbergen Current region. This feature will be re-investigated. Not that

the data hole around 0°E, 74°N makes this investigation unpractical.

Figure 41: Climatology of mixed layer depth (m) during March estimated from (a) TOPAZ model (2002-2014), (b) Nordic Atlas (2002-2012; Korablev et al., 2014), (c) NODC World Ocean Atlas 2013, and (d) NOAA Monthly Isopycnal & Mixed-Layer Ocean Climatology (MIMOC; Schmidtko et al., 2013). Mixed layer depths from the observations (b, c, d) are estimated by a finite density difference method, following Nilsen and Falck (2006).


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IV.2.7 Moorings in the Fram Strait

The AWI operates about 15 moorings in the Fram Strait monitoring the temperature of the

incoming Atlantic waters. The two figures below indicate that the near – surface (50 m)

Atlantic Waters and the core of Atlantic Waters (250 m) approximately are both too cold and

too close to the Spitzberg side.

This cold bias is interpreted as excessive vertical mixing (see above) and should be followed

up with the doubling of the vertical resolution. Note that a different simulation with a higher

order scheme for advection of momentum did not show the cold bias. This will be revisited

with the next model version.

Figure 42: Hovmöller plots of temperature at 50 m in the Fram Strait from the AWI mooring (left) and the TOPAZ4 reanalysis (right). The plots abscissa is oriented West-East: F14 is near Greenland and F1 near Spitzbergen.


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Figure 43: Same as previous figure at 250 m depths.

IV.2.8 Volume transports

In this section, the volume transport of Atlantic Water through sections in the TOPAZ

reanalysis and FREE are analysed and compared to observational values from recent

literature. Four different section have been investigated:

North Iceland Current (1994-2010, Jonsson and Valdimarsson, 2012).

Færøy Current (1997-2008, Hansen et al., 2003, 2010).

Svinøy (1998-2000, Orvik and Skagseth, 2003).

Barents Sea Opening (1997-2001, Ingvaldsen et al., 2004).

Here, Atlantic Water is defined as water with a temperature higher than 5°C and salinity

above 35 PSU except for the Barents Sea Opening, where the definition of Ingvaldsen et al.

(2004) has been adapted in order to make the results comparable. They define Atlantic Water

with temperatures greater than 3°C which they consider adequate because this distinguishes

the Atlantic Water from the outflowing water masses.


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Table 6: Long-term mean Atlantic Water volume transport [Sv] and its standard deviation through sections.

For the Færøy Current, only the the upper 600 m are considered which is in accordance to the

analysis by Hansen et al. (2003, 2010). The studies mentioned above are based on mooring

data over different periods of time, which are generally shorter than the reanalysis period. In

order to account for this, for each section, TOPAZ data of both the reanalysis and FREE have

been selected for the respective period only.

In Table 6, the results of the mean volume transport and its standard deviation for the TOPAZ

reanalysis and FREE as well as the literature values are summarised. For both model runs, the

inflow of Atlantic Water is generally lower than the observations.

It can be seen from the results that the North Iceland current does not contain Atlantic Water

in both the TOPAZ reanalysis and FREE. Compared to the observations, both model runs

show a too little volume transport of Atlantic Water in the Færøy current and the Svinøy

section. The Færøy current seems to be slightly stronger in the free run than in the reanalysis.

Through the Svinøy section, the mean volume transport in the reanalysis is much better than

in FREE, however, it is still too weak compared to the observations. While the standard

deviation and thus the variability in the Færøy current in TOPAZ is similar to the

observations, it is about half in the Svinøy section. In the Barents Sea opening, the TOPAZ

reanalysis fits the observational value while the volume transport in FREE is slightly too

small.

When interpreting these results, it has to be taken into account that only a few model grid

points (e.g. 6 for Svinøy and 8 for the North Iceland Current) are considered for the

calculation of the transport.

More Nordic Seas sections (Class 2 metrics) and volume/heat fluxes comparisons with the

GLO MFC reanalysis from Mercator Ocean can be found in the article by Lien et al. (2016)

including the analysis of trends and seasonal cycles of these fluxes.


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Figure 37: Monthly net Atlantic Water (defined by T>3C, no criterion on salinity) volume transports through the Barents Sea Opening between 71,5°N and 73,5°N. F: Free run. A: ARC MFC Reanalysis. Positive values are towards the East. (From Lien et al., 2016).


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V QUALITY CHANGES SINCE PREVIOUS VERSION

The following changes have been carried out on the December 2019 release:

- An increase of the localization radius for temperature and salinity profiles. This

change has applied to the year 2018 in order to improve the deep water masses.

- A less stringent background cutoff for salinity profiles in fresh waters has been

applied during the year 2018 in order to improve the model salinity in the fresher areas

of the Arctic (i.e. the Beaufort Sea).

Two years 2017-2018 have been reprocessed using more salinity data in fresh waters (the

cutoff salinity has been reduced from 30 to 16 psu) and a broader influence radius both for

salinity and temperature profiles (increasing from 300 km to 700 km). This was motivated by

the comparison of the reanalysis against independent salinity data in the Beaufort Sea (Xie et

al., 2019) and will be documented in the next version of the QuID.


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VI REFERENCES

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G. A. Milne, R. S. Nerem, P. D. Nunn, A. J. Payne, W. T. Pfeffer, D. Stammer, and A. S.

Unnikrishnan. Sea Level Change. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen,

J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley, editors, Climate Change 2013: The

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Geo-physical Research Letters, 32(19), 2005. doi: 10.1029/2005GL024216.

Ezraty, R., F. Girard-Ardhuin, and J. –F. Piollé, 2006: Sea-ice drift in the central Arctic combining

QuikSCAT and SSM/I sea ice drift data-User’s manual, Tech. Rep. V2.0, CERSAT-IFREMER, Brest,

France.

Korablev, A., A. Smirnov, and O. K. Baranova, 2014. Climatological Atlas of the Nordic Seas and

Northern North Atlantic. D. Seidov, A. R. Parsons, Eds., NOAA Atlas NESDIS 77, 122 pp.,

dataset doi: 10.7289/V54B2Z78.

Kwok, R., G. F. Cunningham, M. Wensnahan, I. Rigor, H. J. Zwally, and D. Yi, 2009: Thinning and

volume loss of the Arctic Ocean sea ice cover: 2003-2008, J. Geophys. Res., 114, C07005, doi:

10.1029/2009JC005312.

Lien V.S. et al., An assessment of the added value from data assimilation on modelled Nordic Seas

hydrography and ocean transports, Ocean Modelling (2016),

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Lindsay, R. W., 2013: Unified Sea Ice Thickness Climate Data Record, 1975-2012. Boulder, Colorado

USA: National Snow and Ice Data Center. http://dx.doi.org/10.7265/N5D50JXV

Nilsen, J. E. Ø., and E. Falck (2006), Variation of mixed layer properties in the Norwegian Sea for

the period 1948–1999, Prog. Oceanogr., 70, 58–90, doi:10.1016/j.pocean.2006.03.014.

Nilsen, J. E. Ø., H. Hátún, K. A. Mork, and H. Valdimarsson (2008), The NISE Data Set, Tech. Rep.

08-01, Faroese Fish. Lab., Tórshavn, Faroe Islands.

Raj, R. P., L. Chafik, J. E. Ø. Nilsen, T. Eldevik, and I. Halo (2015), The Lofoten vortex of the Nordic

Seas, Deep Sea Res., Part 1, 96, 1-14, https://doi.org/10.1016/j.dsr.2014.10.011

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QUALITY INFORMATION DOCUMENT For Arctic Physical Reanalysis Product ARCTIC REANALYSIS ... · 2020-04-09 · QUALITY INFORMATION DOCUMENT For Arctic Physical Reanalysis Product ARCTIC_REANALYSIS_PHY_002_003