M. Wenzel and J. Schr¨oter

C ^OMBINED A SSIMILATION OF

GEOSAT, TOPEX/P OSEIDON AND TIDE GAUGE RECONSTRUCTION DATA INTO A GLOBAL OGCM

M. Wenzel and J. Schr¨oter

Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany

Introduction

The global sea level is exceedingly reacting on variations of the climate. A warming of the world ocean or the melting of large continental icesheets for example would lead to a sea level rise that would affect directly a large part of mankind. These effects are reasonable well understood on the global scale but they are still uncertain on regional or even local scale. For the period of the TOPEX/Poseidon altimetric measurements Wenzel and Schr¨oter (2006, 2007) showed that the sea level trends vary substantially in space and time and that they are closely associated to heat and salt anomalies in the ocean. But longer time-series of the global distribution of sea level variability are needed to confirm these results because the climate-induced decadal and secular sea level changes may be concealed by seasonal, annual and interannual variations, which may act as noise masking long-term trends. One step in this direction is to utilize data from the GEOSAT altimetric mission (1987-1989) in combination with the TOPEX/Poseidon data (1993-2000). Both datasets will be assimilated into the global ocean circulation model. Additionally informations from a global sea level reconstruction from tide gauges are employed to overcome the problem with the unknown reference for the GEOSAT data.

References:

Assmann K.M. and Timmermann R. (2005) Variability of dense water formation in the Ross Sea, Ocean Dynamics, 55(2), 68-87, doi:10.1007/s10236-004-0106-7 Church J.A. and N.J. White (2006) A 20th century acceleration in global sea-level rise, Geophys. Res. Lett., 33, L01602, doi: 10.1029/2005GL024826

Conkright M.E. et al. (2002) World Ocean Atlas 2001, Objective Analysis, Data Statistics and Figures, CD-ROM Documentation, National Oceanographic Data Center, Silver Springs, MD, 17pp Gouretski V.V. and Koltermann K.P. (2004) WOCE Global Hydrographic Climatology, A Technical Report, Berichte des Bundesamtes f¨ur Seeschifffahrt und Hydrographie, No. 35, 50pp. + 2 CD-ROM Levitus S. et al. (2005) Warming of the world ocean 1955–2003, Geophysical Research Letters, Vol. 32, L02604, doi: 10.1029/2004GL021592

Maier-Reimer E. and Mikolajewicz U. (1991) The Hamburg Large Scale Geostrophic Ocean General Circulation Model (Cycle 1), Technical Report, 2, Deutsches Klimarechenzentrum, Hamburg Reynolds R. W. et al. (2002) An improved in situ and satellite SST analysis for climate, Journal of Climate, 15, 1609-1625

Schodlok M. P. et al. (2002) On the transport, variability, and origin of dense water masses crossing the South Scotia Ridge, Deep Sea Research II, 49, 4807-4825

Wenzel M. and J. Schr¨oter (2006) Understanding measured sea level rise by data assimilation, in:Proceedings of the Symposium on 15 Year of Progress in Radar Altimitry, SP-614, ESA Publication Division, Noordwijk, The Netherlands, ISBN 92-9092-925-1, ISSN 1609-042X, CD-ROM

Wenzel M. and J. Schr¨oter (2007) The global ocean mass budget in 1993–2003 estimated from sea level change, Journal of physical oceanography, 37(2), 203-213., doi:10.1175/JPO3007.1

Willis J. K. et al (2004) Interannual variability in the upper ocean heat content, temperature and thermosteric expansion on global scales, Journal of Geophysical Research, Vol. 109, C12036, doi: 10.1029/2003JC002260

Corresponding e-mail adresses:

Manfred.Wenzel@awi.de Jens.Schroeter@awi.de

Model / Data

For our purpose we use the Hamburg Large Scale Geostrophic model (LSG, Maier-Reimer and Mikolajewicz 1991). In conjunction with its adjoint this model has been used successfully for ocean state estimation (e.g.

Wenzel and Schr¨oter 2006, 2007). It has 2x2 degree horizontal resolution, 23 vertical layers (varying from 20m thickness for the top layer to 750m for the deepest ones) and the implicit formulation in time allows for a time step of ten days. The utilized global OGCM has a free surface, i.e. it conserves mass rather than volume, and it has the steric effects (thermal expansion, haline contraction) included. This offers the possibility to combine altimeric measurements with hydrographic data in a dynamically consistent manner.

Data used for assimilation GETO GETORC

TOPEX/Poseidon (Jan.1993-Dec.2000; GfZ) X X

SSHA GEOSAT (Jan.1987 - Sep.1989; GfZ) X X

reconstruct from tide gauges (1987-2000; AWI,C+W) X

MSSH SHOM98.2 (CLS) rel. EIGEN-GRACE01S geoid X X

!! constrains the period 1993-2000 only !!

SST Reynolds SST (1987-2000) X X

T/S WOCE Global Hydrographic Climatology X X

(mean) Gouretski und Koltermann (2004)

T/S WOA01 X X

(monthly anomalies)

mean transports heat, freshwater, mass X X

from Siedler et al (edt.): Ocean Circulation and Climate

section data Ross Sea, Weddell Sea from BRIOS model runs X X Assmann and Timmermann (2005), Schodlok et al. (2002)

1986 1988 1990 1992 1994 1996 1998 2000

1 2 3 4 5 6 7 8

[cm]

recon. vs. altim.

GETO vs. altim GETO vs. recon

GEOSAT TOPEX

NOTE:

recon. data are not used for assimilation

spatial stdv of difference

1986 1988 1990 1992 1994 1996 1998 2000

1 2 3 4 5 6 7 8

[cm]

recon. vs. altim.

GETORC vs. altim GETORC vs. recon.

GEOSAT TOPEX

spatial stdv of difference

Fig. 2: Spatial RMS difference between the modelled sea level and the different data sets for ex- periment GETO (left) and GETORC (right). The light red shading gives the corresponding value for the difference between the satellite data and the reconstruction from tide gauges.

1986 1988 1990 1992 1994 1996 1998 2000

-5 -4 -3 -2 -1 0 1 2 3

[cm]

Church+White tide gauges

GEOSAT TOPEX

GETO

global mean SSHA

1986 1988 1990 1992 1994 1996 1998 2000

-6 -5 -4 -3 -2 -1 0 1 2 3 4

[cm]

Church+White tide gauges

GEOSAT TOPEX

GETORC

global mean SSHA

Fig. 1: Global mean sea level anomaly of the experiments GETO (left) and GETORC (right) compared to the different data sets. The GEOSAT data are adjusted to meet the corresponding model mean.

Sea Level: model vs. data

Figure 1 shows that in both experiments, GETO (left) and GETORC (right), the model reproduces the global mean sea level data from the TOPEX data well. For the period of the GEOSAT data the model gives a positive trend consistent with the data from tide gauge reconstruction but contradic- ting the negative trend given by the GEOSAT data. Further- more the assimilation procedure sees no need for a conti- nuous sea level rise without the tide gauge data. Even the spatial RMS of the difference between model and SSH da- ta is improved using this additional information from tide gauges (Fig.2).

-0.00

-0.00

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0.12 0.02

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0.16 -0.04

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 6125.

5375.

4625.

3875.

3250.

2750.

2250.

1750.

1300.

950.0 700.0 512.5 369.0 281.5 228.0 190.5 162.5 137.5 112.5 87.5 63.0 40.5 20.0 0.0

depth [m]

23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

layer index

GETORC : global ocean

layer heat content anomaly [10²³ J]

1988 1990 1992 1994 1996 1998 2000

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

[1023 J]

[0-700] [700-2750]

[2750-bottom] [0-bottom]

Levitus [0-700]

GETORC

global ocean heat content anomaly

Fig. 3: Global heat content anomaly from experiment GETORC given for each layer (left) and summerized for the depth ranges [top 700m], [700m-2750m] and [below 2750m] (right). The right graph also includes the heat content anomaly for the top 700m derived from the Levitus et al (2005) data. NOTE: there are different references in the figures:

the first timestep for the left one and the temporal mean for the right!

Global Heat Content Anomaly

Within the top 100m the modelled global heat content anomaly shows a pronoun- ced annual cycle (Fig.3,left part), while in the deeper ocean two distinct depth ran- ges, [150-700m] and [below 1700m], can be found that show significant warming and will contribute to the global sea level rise via thermal expansion. Summing over the top 700m (Fig.3 right) this warming trend is consistent with the data from Levitus et al. (2005).

1986 1988 1990 1992 1994 1996 1998 2000

-3 -2 -1 0 1 2 3

[cm]

halosteric thermosteric eustatic total

GETORC

GMSLA decomposed

Fig. 5: Global mean sea level anomaly decomposed into its thermosteric, halosteric and eustatic contribution

Global Mean Sea Level Trends [mm/year]

1987 – 2000

1

^st

guess GETO GETORC C+W thermosteric +0.28 +0.64 +0.92

halosteric –.003 +.001 +0.05 total steric +0.28 +0.64 +0.97 eustatic –0.06 +0.09 +1.98

total +0.22 +0.73 +2.95 +3.27

2 2

4 6

2 4 6

8 8

4

4

6

6 2

8 8

4 2

2 0

4 4 6

2

2

2 0

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0 6 0

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2 6

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4 6

2 0

30 60 90 120 150 180 210 240 270 300 330 360 -90

-60 -30 0 30 60 90

-10 -8 -6 -4 -2 0 2 4 6 8 10

mm/year GETORC

local linear trend 1987 - 2000

area RMS: 3.68 area mean: 2.95

total SSHA

2 2

1

3

2 2

2

3

3

0 2

2

2 1

2

0 2

1 0 2

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-1 2

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3 3

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2 1

2

3

30 60 90 120 150 180 210 240 270 300 330 360 -90

-60 -30 0 30 60 90

-5 -4 -3 -2 -1 0 1 2 3 4 5

mm/year GETORC

local linear trend 1987 - 2000

area RMS: 2.18 area mean: 1.98

eustatic SSHA

-4 0

6 0

0

2

8 6

2

4

0 2

-2 -4

0

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30 60 90 120 150 180 210 240 270 300 330 360 -90

-60 -30 0 30 60 90

-10 -8 -6 -4 -2 0 2 4 6 8 10

mm/year GETORC

local linear trend 1987 - 2000

area RMS: 3.45 area mean: 0.92

thermo-steric SSHA

0 -6

2 4 6

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4

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0

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30 60 90 120 150 180 210 240 270 300 330 360 -90

-60 -30 0 30 60 90

-10 -8 -6 -4 -2 0 2 4 6 8 10

mm/year GETORC

local linear trend 1987 - 2000

area RMS: 3.36 area mean: 0.05

halo-steric SSHA

Fig. 4: from left to right: (a) Modeled local sea level trends from GETORC and its (b) eustatic, (c) thermosteric and (d) halosteric component. The contour intervalls are 2 mm/year in (a),(c) (d) and 1 mm/year in (b)

Sea Level Trends

In Fig.4 the modelled total local sea level trend is splitted into its eustatic, thermosteric and halosteric part. Compared to the thermo- and halo-steric trends the eustatic trend varies on very large scales. There is net eustatic sea level rise nearly everywhere and it is highest in the Atlantic (∼3mm/year compared to

∼2.2mm/year global RMS). Both steric components show a higher global RMS (∼3.4mm/year). But in many regions of the world ocean they are opposite in sign thus compensating each other at least by part.

This ends up with a total steric sea level rise that is much smoother in space and more comparable in local strength to the eustatic (∼2.6mm/year RMS).

But the total steric trend show large positive and ne- gative regions. Thus for the global mean sea level (Fig.5, Table on the left) we find the main contributi- on from the eustatic sea level change, which is about twice as strong as the steric. From comparing expe- riments GETO and GETORC (left table) we find that this eustatic trend is induced by the sea level da- ta reconstructed from tide gauges, while the thermo- steric trend is already mainly constraint by using the Reynolds SST data. Furthermore, from Fig.5 one al- so sees that the global eustatic sea level resamples nearly all the ’short term’ temporal variability (an- nual cycle) of the global mean sea level.