NASA/TM-2003-104606, Vol. 24 

Technical Report Series on Global Modeling 

and Data Assimilation 

Volume 24 

Temperature Data Assimilation with Salinity Corrections: 

Validation for the NSIPP Ocean Data Assimilation System 

in the Tropical Pacific Ocean, 1993-1998 

A. Troccoli, M.M. Rienecker, C.L. Keppenne, and G.C. Johnson 

July 2003 
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NASA/TM-2003-104606, Vol. 24 

Technical Report Series on Global Modeling 

and Data Assimilation 

Volume 24 

Temperature Data Assimilation with Salinity Corrections: 

Validation for the NSIPP Ocean Data Assimilation System 

in the Tropical Pacific Ocean, 1993-1998 

Albert0 Troccoli, Michele M. Rienecker 

Goddard Space Flight Center; Greenbelt, MD 

Christian L. Keppenne 

Science Applications International Corporation, Beltsville, MD 

Gregory C. Johnson 

Nutional Oceanic & Atmospheric Administration, Seattle, WA 

National Aeronautics and 

Space Administration 

Goddard Space Flight Center 

Greenbelt, Maryland 2077 1 

July 2003 
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ABSTRACT 

The NASA Seasonal-to-Interannual Prediction Project (NSIPP) has developed an ocean data 

assimilation system to initialize the quasi-isopycnal ocean model used in our experimental 

coupled-model forecast system. Initial tests of the system have focused on the assimilation of 

temperature profiles in an optimal interpolation framework. It is now recognized that correction 

of temperature only often introduces spurious water masses. The resulting density distribution 

can be statically unstable and also have a detrimental impact on the velocity distribution. Several 

simple schemes have been developed to try to correct these deficiencies. Here the salinity field 

is corrected by using a scheme which assumes that the temperature-salinity relationship of the 

model background is preserved during the assimilation. The scheme was first introduced for a zlevel model by Troccoli and Haines (1999). A large set of subsurface observations of salinity 

and temperature is used to cross-validate two data assimilation experiments run for the 6-year 

period 1993- 1998. In these two experiments only subsurface temperature observations are used, 

but in one case the salinity field is also updated whenever temperature observations are available. 

The effectiveness of the Troccoli and Haines scheme is reflected not only in a better salinity field 

but also in an improved temperature field. The root-mean-square difference (RMSD) between 

the assimilation analyses and observations in the equatorial Pacific shows an average 

improvement in the upper 900m of 20% in the salinity field and of 6% in the temperature field. 

The impact of the subsurface assimilation has been also assessed via data retention experiments 

(simulated forecasts). The RMSD diagnostic for these “forecasts” increases only moderately up 

to a 6-month lead, showing the retention for several months of information from the assimilation. 

... 

111 
TABLE OF CONTENT 

L. 

2.1 

2.2 

2.3 

2.4 

2.5 

2.6 

1. ............................................................................................................ INTRODUCTION (1) 

THE MODEL AND ASSIMILATION SYSTEM ........................................................... (2) 

The ocean model (2) 

Model setup (3) .................................................................................................................... 

Data Assimilation System.. (4) 

Salinity increments to preserve water-mass distributions. (5) 

(7) Incremental analysis ...................................................................................................... Description of the assimilation experiments (7) 

............................................................................................................. 

............................................................................................. ............................................. 

.................................................................. 

3. COMPARISON WITH SUBSURFACE OBSERVATIONS .......................................... (7) 

3.1 The 155 W section (8) 

3.2 Cross-validation statistics (12) 

3.3 Temperature-Salinity Relations (14) 

........................................................................................................... ............................................................................................... ....................................................................................... 

4. DATA RETENTION ....................................................................................................... (16) 

5. SUMMARY AND DISCUSSION ................................................................................... (18) 

ACKNOWLEDGEMENTS (19) 

REFERENCES ...................................................................................................................... (21) 

.................................................................................................. 
List of Figures 

1. Horizontal domain decomposition for the Pacific model. The thin lines delineate grid cells. The thick lines 

correspond to the boundaries of each Processing Element (PE) on a 16 x 16 PE lattice. The dark circles show the 

locations of the TAO moorings. ...................................................................................................................... (4) 

2. Schematic representation of the two analyses (right) obtained by combining model and XBT water columns for 

the monotonic-profile case, following TH99. The shaded layers in the lower analysis profile highlight unstable 

water masses introduced by the vertical displacement of the temperature profile without corresponding displacement 

of the salinity profile. 0 is potential temperature. ........................................................................................... (6) 

3. Observation locations are denoted by the rectangles whose vertical sides are proportional to the latitudinal extent 

of the transects. The horizontal line inside the rectangle identifies the equator. The crosses indicate a subset of the 

transects occupied in April-September and used for data retention experiments. There are 69 transects in total, of 

which 35 are used for the data retention experiments. ..................................................................................... (8) 

4. Meridional sections of temperature across the equator at 155"W for Johnson ef al. (2000) (JMRM), TOl, TOIS 

and CNT for the five months presented by JMRM (cf. JMRM's plate 2). The equator is highlighted in white. 

Isolines are thicker for 8, 18 and 28°C. ............................................................................................................ (10) 

5. As in Figure 4 but for salinity. Isolines are thicker for 34, 35 and 36. ..................................................... (1 1) 

6. Root-mean-square difference (RMSD) between the three model runs (TOI, TOIS and CNT) and the observations 

as a function of depth for the 69 available transects in Figure 3: west equatorial Pacific (a, c) and east equatorial 

Pacific (b, d); temperature RMSD (a-b) and salinity RMSD (c-d). ................................................................. (1 3) 

7. Temperature-Salinity diagrams for TOI, TOIS and observations (OBS) for the four regions defined on each 

panel. The color convention is explained in panel c. For instance, the red color means that both TOI and TOIS 

agree with observations. Superimposed dotted lines are, the (r isolines. ........................................................ (15) 

8. As in Figure 6 but for the two assimilation runs, TO1 and TOIS, and the two simulated forecasts, f-TO1 and 

f-TOIS, and for the 35 crossed transects (see Figure 3). ................................................................................. (17) 

9. Temporal evolution of the composite RMSD of the 19°C isotherm depth (a-b) and of the salinity on the 25 

sigma surface (c-d) for all lead times for the control run (CNT), the two assimilation runs (TO1 and TOIS), and the 

simulated forecasts (f-TOI and f-TOIS). The RMSD calculation is split in south (a, c) and north (b, d) of the 

equator. ............................................................................................................................................................ (18) 

Vii 
1. Introduction 

Assimilation of subsurface temperature (7') observations has improved the quality of ocean state 

analyses. When these analyses are used to initialize coupled-model predictions, the forecast skill 

also improves (e.g., Ji et al., 1998, and Segschneider et al., 2001). However, it has been shown 

that the univariate assimilation of temperature has deleterious effects on the salinity (S) field and 

hence on the density field (Cooper, 1988, Acero-Schertzer et al., 1997, Ji et al., 2000, Troccoli et 

al., 2002, the latter hereafter referred to as TBS02). 

Some solutions to this problem have already been put forward. The proposed solutions can be 

broadly divided into two main categories: those that rely on direct observations in order to 

construct some historical correction (Vossepoel et al., 1999, Maes and Behringer, 2000, and 

Vossepoel and Behringer, 2000) and those that draw statistical or physical information from the 

model itself [e.g., through an Ensemble Kalman Filter (EnKF: Keppenne and Rienecker, 2002) or 

model statistics, or from local model background water mass properties as in Troccoli and Haines, 

(1999), hereafter TH991. Given the scarcity of direct salinity measurements, applications of the 

first approach are necessarily limited as their training period can only refer to restricted time-space 

intervals. This restriction reflects on the limited variability contained in the statistics. This situation 

appears unlikely to change for a few years to come, although efforts for a better salinity observing 

network are underway, especially with Argo floats (e.g., http://www.argo.ucsd.edu). On the other 

hand, the more complex primitive equation models, such as the HOPE model at the European 

Centre for Medium-Range Weather Forecasts (ECMWF, see TBS02) and the Poseidon model at 

the NASA Seasonal-to-Interannual Prediction Project (NSIPP, see Coles and Rienecker, 2001), 

have proven capable of simulating the water-mass characteristics quite well. This has allowed the 

development of methods belonging to the second category. However, it is one of the objectives of 

this paper to further validate the water-mass characteristics of the Poseidon model using a 

substantial number of subsurface observations. 

In this work, as in TBS02, the focus is on the use of model-derived water-mass properties (T and 

S) to correct the model salinity commensurate with the temperature corrections made by 

assimilating temperature observations. The salinity increments are calculated according to the 

temperature analysis by preserving the model's local T-S relationships as described in TH99. The 

TH99 (salinity) scheme does not make any use of statistics for the salinity adjustment. Two other 

appealing advantages of this approach are computational costs and model portability. 

An initial evaluation of the TH99 scheme, using a z-coordinate ocean model, was presented in 

TBS02. Subsequently, Segschneider et al. (2001) showed that when the TH99 scheme is added to 

their combined temperature and sea surface height assimilation, it improves the ECMWF seasonal 

forecast on lead times greater than 3 months, and up to 6 months. The same scheme is 

implemented here in a quasi-isopycnal model to test whether a different model formulation would 

also benefit from the TH99 scheme. The validation of the TH99 scheme, undertaken in TBS02 by 

a comparison with climatologies, is extended here through a comparison with an abundant set of 

independent (i.e., not assimilated) observations and by using different diagnostics. As a further 

step, the retention of information - either improved or degraded structure, depending on whether 

salinity is adjusted or not - is assessed in simulated forecast mode, that is, when the assimilation 

ceases but the prescribed time-dependent forcing is continued. This experiment has the advantage 

I 
over coupled forecast tests in that the shock or drift usually found to dominate initial evolution of 

coupled models is not present to confuse the interpretation of results. 

In section 2 the ocean model, the data assimilation system and the experiments are described. 

Comparisons with comprehensive observation analyses are presented in section 3 for the 

assimilation experiments. In section 4, the simulated forecasts are analyzed. Finally, a discussion 

is presented in section 5. 

2. The Model and Assimilation System 

2.1 The ocean model 

The ocean model used in this study is the reduced-gravity quasi-isopycnal Poseidon ocean 

model, Version 4, which uses a generalized vertical coordinate designed to represent a turbulent, 

well-mixed surface layer and nearly isopycnal deeper layers. Coastal topography is represented, 

but the reduced-gravity treatment precludes the use of variable bottom depth. Poseidon has been 

documented and validated in hindcast studies of El Nifio (Schopf and Loughe, 1995) and has 

since been updated to include prognostic salinity (e.g., Yang et al., 1999). More recently, the 

model has been used in an investigation of the annual cycle in the eastern Equatorial Pacific (Yu 

et al., 1997) and in a numerical study of the surface heat balance along the equator (Borovikov et 

al., 2001). 

Poseidon's prognostic variables are layer thickness, h(A, 6, 6 t), temperature, T(A, 6, f: t), 

salinity, S(A, 6, 5, t), and the zonal and meridional current components, u(A, S, 5, t) and 

v(A, S, 5, t), where A is longitude, elatitude, t time and (is a generalized vertical coordinate 

which is 0 at the surface and increments by 1 between successive layer interfaces. 

Explicit detail of the model, its vertical coordinate representation and its discretization are 

provided in Schopf and Loughe (1 995) and are only summarized here. The equation for mass 

continuity is 

ah aw 

at al - + V.(vh) + 3 = 0, (2.1.1) 

where V. and v are the two-dimensional (2D) divergence operator and velocity vector and we 

represents the volume flux across layer interfaces, including freshwater flux through the surface. 

The heat equation is 

(2.1.2) 

where Q is the external heat flux, Fcis a heat diffusivity and Fh is a 2D smoothing operator. The 

salinity equation is 

2 
-+V.(vhS)+L=- aw a [ -- Ks as)+ hF,,(S), ahS 

at 34- 34- hac 

(2.1.3) 

where & is a salinity diffusivity. The 2D momentum equation is 

where vis a vertical friction, zis the vertical shear stress,fkxv is the Coriolis acceleration and 

F, is a dissipation term. A hydrostatic Boussinesq approximation is made, so that if p'( z) is the 

pressure anomaly at depth z, b is buoyancy and po is the mean density, the hydrostatic equation 

then becomes 

(2.1.5) 

Following Pacanowski and Philander (198l), vertical mixing is parameterized through a 

Richardson number-dependent mixing scheme implemented implicitly. An explicit mixed layer 

is included with a mixed layer entrainment parameterization following Niiler and Kraus (1977). 

A time-splitting integration scheme is used whereby the hydrodynamics are done with a short 

time step (15 minutes), but the vertical diffusion, convective adjustment and filtering are done 

with coarser time resolution (half-daily). 

2.2 Model setup 

The version of Poseidon used here has been parallelized as in Konchady et al. (1998) using the 

same message-passing protocol and 2D horizontal domain decomposition used by Schaffer and 

Suarez (2000) for the atmospheric model. Many of the details of the parallel implementation of 

both the model and the assimilation system can be found in Keppenne and Rienecker (2001). 

The model, in a global configuration, is used for NSIPP's coupled forecast system. However, for 

this study the domain was restricted to the Pacific Ocean, from 45"s to 60"N. The horizontal 

resolution was 1 Ox 1 ", plus an equatorial refinement: the meridional resolution changes 

smoothly from 1/3" at the equator to 1" within 10" of the equator (Figure 1). There are 20 layers 

in the vertical. At the southern boundary temperature and salinity are relaxed towards the 

Levitus climatology. There are 173 x 164 x 20 grid boxes, of which 28% are situated over land. 

The model is forced by daily averaged wind stresses derived from the Special Sensor 

Microwave/Imager (SSM/I) by Atlas et al. (1996). The same wind product, but monthly averaged, 

is used to derive the sensible and latent heat components through the atmospheric mixed layer 

model by Seager et al. (1995). The precipitation is given by the monthly averaged analyses of Xie 

and Arkin (1997). An additional relaxation to climatology is applied to the surface salinity field 

with a time scale of nine months. 

3 
60 

50 

40 

30 

20 

10 

0 

-10 

-20 

-30 

40 

140 160 180 200 220 24 0 260 280 

Figure 1. Horizontal domain decomposition for the Pacific model. The thin lines delineate grid cells. The thick 

lines correspond to the boundaries of each Processing Element (PE) on a I6 x 16 PE lattice. The dark circles show 

the locations of the TAO moorings. 

2.3 Data Assimilation System 

The data assimilation system in this study is composed of two parts: a univariate optimal 

interpolation system (UOI) to calculate the temperature analysis increments, followed by the 

TH99 scheme to calculate salinity increments consistent with the temperature analysis. The 

temperature analysis is generated according to 

(2.3.1) 

xu =x'+P'"Tb. (2.3.2) 

In (2.3.1) and (2.3.2), uppercase boldface symbols represent matrices, and lowercase boldface 

symbols represent vectors. The vector d (nd x 1) contains nd observations; x (n, x 1) is the state 

vector. The superscripts a andfrefer to the analyzed state and the forecast, respectively. The 

matrix W (nd x nd) is the observational error covariance matrix. The representer matrix, 

[HP-/HT+W] b=d-Hx-/, 

4 
R = HP’HT, maps the background (forecast) error covariance matrix, Pf (n, x n,), to the error 

subspace of the measurements. The elements of b are the representer-function amplitudes used 

to update x. The nd x I vector, z =d-Hx’, contains the innovations. 

In this application, the measurement functional, Hx, is simply a 3D interpolation operator which 

maps the model temperature field, assumed to be at the center of the model layer, to the latitude, 

longitude and depth of each observation. The background-error covariance used in the UOI is 

constant in time. Here the function depends only on the distance between forecast locations, and 

a Gaussian functional form is chosen: 

Pf(AkA$,&) = Cexp(-(AA/L,)2 -(A$/LJ2 - (&/L,)’}, 

where La defines the zonal decorrelation scale, L4 the meridional decorrelation scale and L, the 

vertical decorrelation scale. In this application, LA = 1800 km, Ld = 400 km in the equatorial 

waveguide, and L, = 50 m. The horizontal scales are consistent with those used by Ji et al. 

(1995). The value for La is modulated meridionally as suggested by Derber and Rosati (1989) to 

shorten the covariance scales with latitude. 

2.4 Salinity increments to preserve water-mass distributions. 

Troccoli and Haines ( 1999) demonstrated the deleterious effects of assimilating temperature 

profiles without making corresponding adjustments to the salinity profile at each model grid 

point influenced by the temperature assimilation. The problems arise from the introduction of 

gravitationally unstable profiles through the generation of new water masses (e.g., Figure 2). 

They proposed a scheme to preserve the water-mass distribution of the model prior to 

assimilation. The idea stems from the fact that vertical displacements of the water column, 

because of internal wave motion or the passage of mesoscale features, can occur without 

significant changes in the water mass properties. Even for the case of non-monotonic S=S(T), 

two (or more) isothennic parcels can be distinguished according to their depth and the TH99 

scheme recovers salinity from the nearest T(z) in the background field. 

The scheme is presented fully in TH99 and TBS02 and is presented only briefly here. The 

procedure is applied at each model grid point in two steps. First, a vertical displacement of the 

model T background profile to match the deepest analyzed Tis made. The same displacement is 

applied to the S profile, too. Second, the scheme computes an S increment using the T-S 

relationships from the model T- and S-background profiles and the analyzed T, at each grid point, 

according to the following formulation: 

where Azis a specified depth tolerance, which could be a function of location. In our case we 

chose a fixed Az =loom. Subscripts an and bg stand for analysis and background, respectively, 

and qg = dTbg=Tan) is the depth at which the background temperature is the same as the analysis 

5 
temperature. In case of multiple dTbg=T,n) solutions, the nearest depth to Gn is considered. 

Also, as the T-S preservation assumption generally does not hold near the surface, the salinity is 

not updated in the surface isothermal layer. 

110. 34.8) 

::>: ’,‘: ’ ‘ .; , 5 

(5. 34.5) 

Model XBT I (28, 34.2) 1 (28) ............ ......... 

(25, 35.2) ....................... 

(20) 

(20, 35.0) 

./‘ (15. 34.9) 

(10. 34.8) 

(5. 34.5) 

.......... ...... ...... 

preservation 

so persistence 

Anal sis oo(kg m”) 

21.80 

(1 5. 34.9) 25.90 

27.28 

l(28. 34.2) I 21.80 

23.49 

24.92 

24.76 

25.97 

25.90 

26.87 

26.80 

27.52 

27.28 

Figure 2. Schematic representation of the two analyses (right) obtained by combining model and XBT water 

columns for the monotonic-profile case, following TH99. The shaded layers in the lower analysis profile highlight 

unstable water masses introduced by the vertical displacement of the temperature profile without corresponding 

displacement of the salinity profile. 8 is potential temperature. 

Thus, the TH99 scheme only needs three easily retrievable ingredients: (1) the analyzed 

temperature profile, (2) the model temperature, and (3) salinity profiles for each model grid 

point. The analyzed temperature profile can be the result of any analysis method such as optimal 

interpolation. 

6 
2.5 Incremental analysis 

Incremental analysis updating (IAU, e.g., Bloom et al. 1996) is used to insert the analysis 

increments, x - x.f , into the model in a gradual manner. Namely, the model partial differential 

equations (2.1.1-2.1.4) are replaced with 

(X‘(ti)-XJ’(ti)) , t; I t < ti+,, ax 

at (t;+, - t;) - = F(x, t) + (2.5.1) 

where F stands for the right hand sides of (2.1.2-2.1.3) and xu (ti ) and x (ti ) are the analysis 

and forecast at the time, ti, of the ith analysis. 

Unlike nudging (e.g., Daley 1991), which relaxes the model state toward an analysis, the analysis 

increments are inserted as a state-independent forcing term. The IAU has properties similar to 

those of a low-pass filter and can improve observed-minus-forecast statistics with respect to a 

non-incremental updating scheme (Bloom et al. 1996). 

The IAU is used here for two reasons. First, it lessens the unwanted effects of intermittent data 

assimilation, specifically initialization shocks resulting from imbalances between the model 

fields following the direct insertion of the analysis increments. Second, the IAU allows the 

model to gradually adjust the h field in response to the T and S increments without violating the 

constraints imposed by the continuity equation (2.1.1). 

2.6 Description of the assimilation experiments. 

The subsurface temperature measurements employed in this study include all the observations 

available in real-time from the Global Telecommunication System, including XBTs (expendable 

BathyThermographs), TAO (Tropical Atmosphere-Ocean) mooring data (e.g., McPhaden et al., 

1998), and profiling floats. The observations were quality controlled at the National Center for 

Environmental Prediction (NCEP). Two experiments have been run to examine the ocean 

structure evolution. For the first, only temperature is updated (experiment TOO. For the second, 

salinity as well as temperature is updated (experiment TOZS), with the salinity increments given by 

the TH99 scheme. For reference, a control run with no data assimilation (experiment CNT) is used 

to check how the data assimilation affects systematic model errors. The three runs all use the 

ocean model set-up described in section 2.2. The initial conditions were taken from a spun up 

simulation where climatological forcing was applied for 10 years and then daily forcing from 1988 

onwards. The experiments in this study were run for the 6-year period 1993-1998. 

3. Comparison with subsurface observations 

Subsurface salinity observations are rather scarce and so it is generally difficult to validate model 

and/or assimilation results in terms of the salinity field. A recent paper by Johnson et al. (2000) 

(hereafter JMRM) provided a thorough data analysis of salinity from Conductivity-TemperatureDepth (CTD) observations for the equatorial Pacific for the period from September 1996 to 

7 
November 1998. That dataset has since been extended. This study uses all the CTD observations 

available for the period 1993-1998. The transects are located in the zone between 165"E and 

95"W and normally extend from 8"s to 8"N (see Figure 3). The temperature and salinity 

accuracies are 0.002"C and 0.003', respectively. The JMRM observations were mapped onto a 

high resolution grid with 1/5" meridional spacing and 10m vertical spacing. In the following, it 

should be borne in mind that the observed fields are gridded quasi-synoptic one-time sections 

whereas for the model fields we will consider monthly averages. 

1 

0 bserva t io n s I oca tio n s 

9981 8 

997 I" # 

996 1 

El 

% 

tl '8 h 

B 

E I 

a 4 

8 11997 

El lgg

i1993 1 ,; 3 8, ,grgg3 

160E 180 160W 140W 12OW IOOW 

Longitude 

Figure 3. Observation locations are denoted by the rectangles whose vertical sides are proportional to the latitudinal 

extent of the transects. The horizontal line inside the rectangle identifies the equator. The crosses indicate a subset of 

the transects occupied in April-September and used for data retention experiments. There are 69 transects in total, of 

which 35 are used for the data retention experiments. 

3.1 The 155"W section 

The physical structures of the analyses from the different experiments have been compared with 

that from JMRM's analysis. Here, the focus is on the meridional section at 155"W. This choice is 

motivated by good temporal and spatial coverage (Figure 3), by location (the central Pacific), and 

by the strength of the salinity signal (there is a pronounced salinity tongue). 

Salinity is unitless, as defined by the 1978 Practical Salinity Scale. I 

8 
Let us first consider the temperature field, retrieved from the same CTD casts as salinity, so as to 

appreciate the modifications introduced by the 01 procedure. Figure 4 shows the meridional 

transects across the equator at 155"W for JMRM's analysis, TOZ, TOZS and CNT (from left to right, 

respectively) for the five months presented by JMRM. We will mainly focus on two features: the 

vertical temperature gradient and the meridional thermocline gradient. 

The thermocline is considerably tighter in the observation, than in the control run, CNT, especially 

in November 1997. Observed gradients are up to 1.5"C over 1Om. In the TOZ and TOZS 

assimilation runs the thermocline becomes tighter than in CNT, and hence closer to the 

observations. Yet, the vertical gradient in TOZ and TOZS is not as strong as in the observations. 

The difference is mainly due to the fact that monthly averages of the model fields are being 

considered. In fact, other analyses have shown that the vertical displacement of the thermocline on 

even shorter time scales (about 10 days) can be as large as 40m or more, which can translate to a 

temperature variability of about 2.5"C (cf. Figure 2 in TH99). Similar considerations will also 

apply to the salinity field for which the same displacements can lead to a salinity variability of up 

to about 0.2. 

Taking the 18°C isotherm as a reference, it can be seen that the meridional gradient of the 

thermocline is reasonably well simulated by the control run (compare the first and last columns of 

Figure 4). The main contribution of the assimilation runs is in the region of the pronounced 

thermocline ridge near 6-8"N where both meridional and vertical gradients are sharpened by the 

assimilation. Another feature worth noting is the bulge present in the 12°C isotherm in some of 

the TOZ transects, such as at 4"s in December 1996 and at the equator in November 1997. We will 

come back to this point later in this section. 

The salinity fields for the same five transects are presented in Figure 5. For the comparisons of 

salinity structure we focus on three regions: the subsurface both south (Le., the salinity tongue) 

and north of the equator, and the upper water column. The shape of the salinity tongue is well 

captured by CNT. Even the excursions of the 35 isohaline across the equator during 1997 (an El 

Nifio year) are reproduced reasonably well. However, the values are often 0.2-0.4 higher than 

observed. North of the equator, the subsurface salinity structure is rather uniform and close to 

observations, although north of about 4", the observed values are normally underestimated by 

about 0.2 in the model. Near the surface, differences are less systematic. South of the equator, 

CNT is often saltier than observed; however, except for poleward of 4"s in November 1997, 

differences are within 0.2 to 0.4. 

The salinity tongue comparison between TOZ and TOZS shows that, although the high salinity 

values are present in both runs, the shape of the tongue is generally too diffuse in the TOZ run. In 

comparison, TOZS captures the observed features better. Salty water observed in the lower 

thermocline is associated with eastward advection of salty water in the Equatorial Undercurrent 

(EUC). This contrasts with surrounding westward advection of fresher water in the South 

Equatorial Current (JMRM). Similar features but with a smaller magnitude are also present in 

TOZS and CNT. A striking feature is that in all the panels for the TOI run, a column of salty water 

(S > 34.8) extending a few hundred meters below the thermocline is prominent just south of the 

equator. 

9 
OBS (JMRM) 

oc: 98 

Temperature at 155"W 

TO1 

':e: 98 

CNT 

Uct 38 

Figure 4: Meridional sections of temperature across the equator at 155"W for Johnson et al. (2000) (JMRM), TOI, 

TOZS and CNT for the five months presented by JMRM (cf. JMRM's plate 2). The equator is highlighted in white. 

Isolines are thicker for 8, 18 and 28°C. 

10 
Salinity at 155"W 

OBS (JMRM) 

Oct 98 

0 

50 

h 100 

&, 150 

2 250 

300 

350 

400 

g 200 

-864-20 2 4 6 8 

Jun 98 

TO I 

Oct 98 

TOlS 

Oct 98 

C NT 

Oct 98 

-86-4-20 2 4 6 8 

Jun 98 

-86-4-20 2 4 6 8 

Jun 98 

-86-4-20 2 4 6 8 

Jun 98 

-4-20 2 4 6 8 

Nov 97 

-4-20 2 4 6 8 

Nov 97 

-4-20 2 4 6 8 

Nov 97 

-86-4-20 2 4 6 810 

May 97 

-8~420 2 4 6 810 

May 97 

-86-420 2 4 6 810 

May 97 I -864-20 2 4 6 810 

Mov 97 

0 

50 

,--. 100 

&, 150 

g 200 

250 

300 

350 

400 

-86420 2 4 6 8 

Dec 96 

-864-20 2 4 6 8 

Dec 96 

-864-20 2 4 6 8 

Dec 96 

-864-20 2 4 6 8 

Dec 96 

-86-4-20 2 4 6 810 

Lot (0) 

-86-4-20 2 4 6 810 

Lot (") 

-864-20 2 4 6 810 

Lot (0) 

-86-4-20 2 4 6 810 

Lot (0) 

Figure 5: As in Figure 4 but for salinity. Isolines are thicker for 34, 35 and 36. 

11 
Such features are common in cases in which temperature is assimilated with no salinity correction, 

in areas with a marked salinity gradient, as discussed by the stability analysis in TBS02. As a 

result, gravitationally unstable conditions, which cause warm and salty water to be entrained at 

depth, are spuriously introduced in the model. This is an undesirable feature as the spurious water 

mass may persist in the model for a long time. On the other hand, with TOZS, the marked columns 

of salty water seen in TOZ have almost entirely disappeared and TOZS is closer in character to the 

observations. 

North of the equator, TOZ and TOZS do not differ much from each other and both suffer from a 

fresh bias. This bias, as observed earlier, is a legacy of the control run and therefore it is not 

possible to eliminate it with the adopted assimilation approach. 

It is interesting to note that in TOZS, the near-surface salinity is generally better simulated than in 

both CNT and TOZ, even though it is not corrected. This is probably a reflection of the improved 

subsurface salinity structure which may be entrained into the surface layer during mixing. In fact, 

the surface forcings are essentially the same as those in TOZ, and differ from CNT solely by the 

evaporation component which, however, does not seem to play a dominant role (compare TOZ and 

CNT). Note also that the three runs are a11 subject to the same sea surface salinity relaxation. Even 

the surface zonal current is reproduced better by TOZS. 

3.2 Cross-validation statistics 

A cross-validation against all available JMRM temperature and salinity observations was 

conducted by calculating the root-mean-square difference (RMSD) for the three runs (Figure 6). 

For each model variable we consider the nearest observation value. The 69 transects available are 

then grouped into two sets: west equatorial Pacific (WEP), for longitudes between 160"E and 

150"W, and east equatorial Pacific (EEP) between 150"W and 90"W. 

As expected, the temperature RMSD is notably reduced in the two assimilation runs with respect 

to the CNT run (Figures 6a, b). This reduction is more accentuated in the EEP, where the RMSD 

in CNT is larger, by more than OS"C, than in either TOZ or TOZS in most of the thermocline, that is 

from 50m to 250m. The reduction is marked even in the WEP but the magnitude is generally 

halved. Some small differences appear also between TOZ and TOZS at thermocline depths, with 

TOZS generally closer to observations, especially between 50m and lOOm in the EEP where the 

two RMSD differ by up to 0.25"C. More important, however, are the differences between TOZ and 

TOZS below 50m. This is especially valid for the EEP since in the WEP the differences are 

marginal. In the EEP, in fact, the differences reach about 0.3"C at 450m. The larger RMSD for 

TOZ reflects the worsening of the thermal structure below the thermocline as a consequence of the 

instabilities introduced as discussed above. Therefore, it is a positive feature of the TH99 scheme 

that by changing the salinity field, even the thermal structure is improved. The TOZS reduces the 

temperature RMSD with respect to CNT by a considerable 17%. 

12 
200 - 

- 

E - 400- 

5 

Q 

a, 

0 

600 - 

800 - 

WEST PACIFIC [ 160"E- 1 5O"W] 

Tern pera t u re 

TOIS 

EAST PACIFIC [ 1 50+90"W] 

Tern pera t u re 

0 1 2 3 4 

rms T ["C] 

Salinitv 

0 

200 

400 

600 

800 

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 

rms S rms S 

Figure 6: Root-mean-square difference (RMSD) between the three model runs (TOI, TOIS and CNT) and the 

observations as a function of depth for the 69 available transects in Figure 3: west equatorial Pacific (a, c) and east 

equatorial Pacific (b, d); temperature RMSD (a-b) and salinity RMSD (c-d). 

It is very encouraging that the salinity RMSD in TOZS is systematically smaller than in TOZ in both 

the EEP and the WEP (Figures 6c, d). Their difference reaches a peak of 0.16 at 50m in the WEP 

and 0.1 at 350m in the EEP. Note that below 250m in the EEP, the salinity RMSD trend largely 

reflects that for the temperature RMSD, again a consequence of the enhanced mixing which 

entrains saltier water at depth (see also the next section 3.3). The improvement of TOIS compared 

to TOZ is relatively larger for salinity than for temperature. Considering the two regions, WEP and 

EEP, together, we obtain an average of 6% improvement for temperature and 20% for salinity. 

The salinity RMSD for TOZS is also marginally better than that for CNT, with an average 

improvement of 3%. 

13 
As mentioned above, the current structure is often also improved with TOIS. RMSD statistics 

calculated using ADCP observations for the EEP region showed an improvement in TOZS of 10% 

and 5% with respect to TOZ and CNT, respectively. However, the improvements in zonal velocity 

are generally less than the uncertainty (5cm s-') in the observations. 

3.3 Temperature-Salinity Relations 

The detrimental impact of the TOZ on the water-mass distribution is apparent in a comparison of 

T-S pairs for the observations, TOZ and TOZS. Since the T-S characteristics differ between north 

and south of the equator, we further divide the two regions, EEP and WEP, considered above. We 

therefore account for four regions as presented in Figure 7. 

The differences in T-S distributions between regions is clear from Figure 7. The T-S relation in the 

north WEP is generally tighter than in the other regions, with very little variation in salinity, but 

with a slight subsurface saline maximum associated with North Pacific Tropical Water (NPTW) 

and South Pacific Tropical Water (SPTW) which penetrates across the equator (see Johnson and 

McPhaden, 1999). The subsurface salinity maximum of the SPTW is a significant feature in the 

south WEP. In the south EEP, although the water is fresher at depth, the subsurface salinity 

maximum is less apparent because the surface waters are more saline in this region outside the 

South Pacific and Intertropical Convergence Zones. Hence, even in this narrow region, the TH99 

scheme is tested in different T-S regimes. 

Figure 7 is produced by considering the T-S pairs at each observation location and the model 

values interpolated to the observation locations. The T-S pairs are accumulated on a T-S grid 

whose granularity is 0.25"C by 0.1. Where at least one T-S pair exists, a colored circle is plotted. 

This implies that each T-S grid point is given the same weight. However, by neglecting T-S grid 

points with a low occupancy number, we checked that the features shown in Figure 7 are robust. 

The red color signifies that the T-S characteristics of all three ocean representations, TOZ, TOZS and 

the observations, coincide. Manifestly, red is the predominant color in all four regions, implying 

that the two assimilation runs produce a good representation of the observed ocean. However, 

other features are also apparent. North of the equator (Figures 7a, b), the assimilation fields (as 

well as the control run, not shown) have fresher values than observations as seen from the 

abundance of violet circles for low salinities and, concurrently, from the cyan circles on the higher 

salinity range. These values mostly relate to fresher surface waters, often mixed into the upper 

thermocline (see the sections at 155"W for example) and result from deficiencies in the surface 

freshwater flux. In the north EEP, the fresher north Pacific influence in the model is also apparent. 

The few cyan circles in the figures for the northern hemisphere also indicate model deficiencies: 

in the penetration of NPTW equatorward in the WEP, and in the cross-equatorial penetration of 

SPTW in both WEP and EEP. There are very few black circles to indicate problems in the North 

Equatorial Pacific. In this region, variations in salinity in the equatorial waveguide are mainly in 

the surface layer, so there is little that the TH99 scheme can (or has to) do to rectify the model's 

salinity as the temperature is changed through assimilation. 

14 
NORTH-WEST r 1 ~OOE+I 500~- > OONI NORTH-EAST r i50+900w- > OONI 

I I I 11.1 I I I I.'l I I I I I I I I 111'1 1 I I I 'I I I I .I I1 I ,I1 I I I I I' I I I I I'L 

31 32 33 34 35 36 37 

Sa I in ity 

SOUTH-WEST [I 60"E+ 150"W- < OOSl 

30 

25 

,--. 

0 

e 20 

2 

? 

E 15 

AQ 

+ 

10 

5 .' 

I I I 11'1 I I I I IL.1 I I I 11.1 I I I I 11.1 I I I 11.1 I I I .I I I I I III I I1 11. I I I I ,.'I I I1 

31 32 33 34 35 36 37 

Salinity 

5t .. I I I 11'1 I1 I I.', I I I 11.11 I I1I.I I I I I 'I I I I I .1 I I 1111 I I I I I' I I I 11.1 I I I 

31 32 33 34 35 36 37 

Salinity 

SOUTH-EAST rl 50+90"W- < 0"Sl 

5t .(' I ., I I I I1 I I I I I IL.1 I1 I 11.1 I I I, 11.1 I I I 11.1 I I I I .1 I I I 1111 I I I 11. I I I 11.1 I I I 

31 32 33 34 35 36 37 

Salinity 

Figure 7: Temperature-Salinity diagrams for TOI, TOIS and observations (OBS) for the four regions defined on each 

panel. The color convention is explained in panel c. For instance, the red color means that both TO1 and TOIS agree 

with observations. Superimposed dotted lines are the 0, isolines. 

15 
South of the equator the representation of T-S in the TOZS model is quite good (Figures 7c, d). On 

the other hand, a significant volume of cool, saline water is readily apparent in the TOZ fields, as 

shown by the numerous black circles. These circles show that the problems associated with the 

univariate assimilation of temperature with no salinity correction are most significant in the 

southeastern Tropical Pacific. However, in the western basin (north and south) black circles are 

also noticeable on the fresh side of the T-S pairs. 

4. Data Retention 

The obvious question arises as to whether the improved states from TOZS have any impact on 

seasonal forecast skill. As an initial attempt at answering this question, we have conducted forced 

ocean experiments, to be regarded as simulated forecast experiments, but with “perfect” surface 

forcings. These experiments have the advantage of avoiding the problem of initialization shocks 

and drifts of coupled ocean-atmosphere forecasts. The assimilation fields are used as initial 

conditions for the simulation, but after initialization no further assimilation has been undertaken. 

The experiments thus measure the retention of the information provided by assimilation and 

whether the undesirable features of the TOZ states impact the subsequent ocean evolution or are 

ameliorated by ocean forcing and mixing. The error statistics from these simulations are compared 

with those from a pure simulation mode (the control) and from the continued assimilation case. 

These simulated forecast experiments were taken from the six April 1 dates of 1993 to 1998 and, 

in each case, are integrated for a six month duration. 

We analyze the results of these forecasts by means of two diagnostics. The first uses RMSD as in 

Figure 6, whereas the second considers the RMSD for two specific subsurface variables as a 

function of lead time. 

The RMSD diagnostic as a function of depth is shown in Figure 8 and now includes only the 35 

transects that fall within the 6-month forecast interval, April to September (crossed transects in 

Figure 3). All the months have equal weighting in the RMSD calculation. The simulated forecasts 

initialized by TO1 (TOIS) analyses are labeled as f-TOI, (f-TOZS). The overall results are hardly 

distinguishable from those in Figure 6, indicating that the information in the initial conditions is 

retained for at least 6 months. Only at thermocline depths in the EEP is there a notable increase in 

the RMSD by about 0.2”C for both f-TOZ and f-TOZS, compared to TOZ and TOZS, respectively 

(Figure 8b). Encouragingly, the f-TOZS salinity RMSD almost overlaps that for TOZS, implying 

that the subsurface information is retained on the timescales considered. The same holds true for 

the f-TOZ-TOZ pair, but as for the assimilation runs, the absolute RMSD values are markedly 

larger than for the f-TOZS-TOZS pair. 

To investigate the evolution of the errors with time, we analyzed the RMSD for the depth of the 

19°C isotherm depth (Figures 9a, b) and for the salinity on the 25 sigma-theta surface (Figures 9c, 

d). These variables were chosen because, unlike the 20°C isotherm and the 24.5 sigma-theta 

surface representing the equatorial thermocline, they do not outcrop in the 35 transects. Over the 

six-month lead times, the 35 transects are thus distributed: 5,6, 2,4,7 and 11. Ultimately, 

without further assimilation, the states from f-TOZ, f-TOZS, and CNT would eventually evolve to 

be quite similar (within predictability limits). 

16 
WEST PACIFIC [ 1 6OoE+1 50°W] 

Tem Dera ture 

EAST PACIFIC [ 1 50t9O0W] 

Temperature 

0 1 2 3 4 0 1 2 3 4 

rrns T ["C] 

Salinity Salinity 

rrns T ["C] 

0 

200 

400 

600 

800 

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 

rrns S rrns S 

Figure 8: As in Figure 6 but for the two assimilation runs, TOI and TOIS, and the two simulated forecasts, f-TO1 and 

f-TOIS, and for the 35 crossed transects (see Figure 3). 

However, figure 5 clearly shows that the improved initial conditions provided by both 

assimilations are retained for at least six months. The TOZ states produce a better simulation than 

the control, except for the salinity north of the equator. The improved state resulting from TOZS is 

also generally retained throughout the forecast period, so we conclude that the correction of 

subsurface salinity should have a positive impact on seasonal forecast skill. South of the equator, 

the improvements in the state estimate are retained for six months and generally the salinity errors 

introduced by TOZ seem to be ameliorated somewhat once the assimilation is stopped. North of 

the equator, however, the errors in salinity introduced by the assimilation persist through the six17 
month period. This would indicate that direct salinity observations north of the equator would be 

beneficial. 

SOUTH 

RMSD of Depth of 19.0"C isotherm (in rn) 

4 0 

A0 

+ I 

A 

w( 

+ ? + 

a 

)I( 0 

20/ 15 

5t A 

0 

ot I I I I I I 

1 2 3 4 5 6 

(APR) (MAY) (JUN) (JUL) (AUG) (SEP) 

RMSD of Salinity on sigma 25.0 kg rn-' 1 

(c) 

0.5 ' 

I 

0.0 

1 2 3 4 5 6 

(APR) (MAY) (JUN) (JUL) (AUG) (SEP) 

Lead time (mth) 

NORTH 

RMSD of Depth of 19.0"C isotherm (in rn) 

20 + 

+ 

0 

A *$a 

5 

0 ; 

1 2 3 4 5 6 

(APR) (MAY) (JUN) (JUL) (AUG) (SEP) 

RMSD of Salinity on sigma 25.0 kg rn-' I , 

0.4 1 

fi*Q + fh + + 0.2 "'1 

0.1 1 

0.0 - 

91 + 

@+ 

f 

0 TOIS 

1 2 3 4 5 6 

(APR) (MAY) (JUN) (JUL) (AUG) (SEP) 

Lead time (mth) 

Figure 9. Temporal evolution of the composite RMSD of the 19°C isotherm depth (a-b) and of the salinity on the 25 

sigma surface (c-d) for all lead times for the control run (CAT), the two assimilation runs (TOI and TOIS), and the 

simulated forecasts (f-TOI and f-TOIS). The RMSD calculation is split in south (a, c) and north (b, d) of the equator. 

5. Summary and discussion 

The general applicability of the salinity correction scheme documented in TH99 and TBS02 is 

tested here in an isopycnal model rather than a z-coordinate model. The scheme is validated more 

stringently by comparison with a large volume of time-varying temperature and salinity 

observations collected along 69 transects across the equatorial Pacific over the 6-year period 1993- 

1998 (Johnson et al., 2000, plus subsequent extension). This scheme has been implemented in the 

data assimilation system employed for NSIPPs routine forecasts. 

18 
Even within the equatorial waveguide, the TH99 scheme is tested in different T-S configurations, 

with variations in salinity structure across the equator and along the equator as seen in Johnson and 

McPhaden (1999). The root-mean-square differences between the assimilation estimates of the 

salinity and temperature fields and these observations offer an encouraging assessment of the 

salinity-correction scheme when compared to a conventional 01 procedure that does not update 

salinity. For instance, the improvement in the salinity field is 20% when the TH99 scheme is used 

compared to when only the temperature field is updated. Another evaluation that was carried out 

with this work is that of the impact of subsurface initializations on simulated forecasts. We 

showed that both the subsurface salinity and temperature benefit from the TH99 scheme for all 

lead times considered (up to 6 months) in the south equatorial Pacific. The north equatorial Pacific 

has proven to be a more difficult region to improve, given the presence of systematic errors in the 

model. It is perhaps this region that could most benefit from direct salinity observations. 

Whether the TH99 salinity scheme performs better than other approaches such as multivariate 01, 

based on observations (e.g., Maes and Behringer, 2000) or model-based covariances, or the 

Ensemble Kalman Filter is a matter that will be assessed in the near future. It is apparent though 

that, given its simplicity, the TH99 scheme is effective and easily implemented in any ocean 

model. Also, it does not need any observation or model climatology that can limit the variation 

spread. However, the TH99 scheme strongly relies on the model dynamics to get a good salinity 

field reconstruction and therefore it can only be applied to ocean models that simulate the 

distribution of water masses reasonably well. It has been shown in this study, as well as in TBS02, 

that the two primitive equation models in question, Poseidon and HOPE, satisfy this requirement 

well (although improvements are needed in the northern equatorial Pacific). It might also be 

argued that the success of such a simple scheme lies in its aim of preserving the density balances - 

through the conservation of the isopycnal layering - present in the model prior to assimilation. It 

appears that initializing the model with a balanced density field is crucial in a sequential data 

assimilation framework. The TH99 scheme could therefore be employed by even more complex 

assimilation systems for initialization purposes. 

Acknowledgements 

Computer time was provided by the NASA Center for Computational Sciences at NASNGoddard 

Space Flight Center. This research was supported by RTOP 622-48-04 from the NASA Physical 

Oceanography Research and Analysis Program. GCJ was supported by the NOAA Office of 

Oceanic and Atmospheric Research, the NOAA Office of Global Programs, and the NASA 

Physical Oceanography Program. Temperature observations for assimilation were quality 

controlled and provided by Dr. David Behringer at NCEP. 

19 
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23 
Previous Volumes in This Series 

Volume 1 

September I994 

Volume 2 

October 1994 

Volume 3 

December I994 

Volume 4 

January 1995 

Volume 5 

April I995 

Volume 6 

April I995 

Volume 7 

September I995 

Volume 8 

March 1996 

Volume 9 

March 1996 

Volume 10 

April I996 

Documentation of the Goddard Earth Observing System 

(GEOS) general circulation model -Version 1. 

L.L. Takacs, A. Molod, and T. Wang 

Direct solution of the implicit formulation of fourth order 

horizontal diffusion for gridpoint models on the sphere 

Y. Li, S. Moorthi, and J.R. Bates 

An efficient thermal infrared radiation parameterization for 

use in general circulation models 

M.-D. Chou and M.J. Suarez 

Documentation of the Goddard Earth Observing System 

(GEOS) Data Assimilation System - Version 1. 

James Pfaendtner, Stephen Bloom, David Lamich, 

Michael Seablom, Meta Sienkiewicz, James Stobie, 

and Arlindo da Silva 

Documentation of the Aries-GEOS dynamical core: Version 2. 

Max J. Suarez and Lawrence L. Takacs 

A Multiyear Assimilation with the GEOS-I System: 

Overview and Results 

Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu, 

Wayne Higgins, Yelena Kondratyeva, Andrea Molod, 

Lawrence Takacs, Michael Seablom, and Richard Rood 

Proceedings of the Workshop on the GEOS-I Five-Year 

Assimilation 

Siegfried D. Schubert and Richard B. Rood 

Documentation of the Tangent Linear Model and its Adjoint 

of the Adiabatic Version of the NASA GEOS-I C-Grid GCM: 

Version 5.2 

Weiyu Yang and I. Michael Navon 

Energy and Water Balance Calculations in the Mosaic LSM 

Randal D. Koster and Max J. Suarez 

Dynamical Aspects of Climate Simulations Using the GEOS 

General Circulation Model 

Lawrence L. Takacs and Max J. Suarez 
Volume 11 

May I99 7 

Volume 12 

August 1997 

Volume 13 

December 199 7 

Volume 14 

August I998 

Volume 15 

June I999 

Volume 16 

November 1999 

Volume 17 

July 2000 

Volume 18 

December 2000 

Volume 19 

July 2001 

Volume 20 

August 2001 

Volume 21 

September 2001 

Documentation of the Tangent Linear and its Adjoint Models 

of the Relaxed Arakawa-Schubert Moisture Parameterization 

Package of the NASA GEOS-I GCM - Version 5.2 

Weiyu Yang I, Michael Navon, and Ricardo Todling 

Comparison of Satellite Global Rainfall Algorithms 

Alfred T.C. Chang and Long S. Chiu 

Interannual Variability and Potential Predictability in 

Re-Analysis Products 

Wie Ming and Siegfried D. Schubert 

A Comparison of GEOS Assimilated Data with FIFE Observations 

Michael G. Bosilovich and Siegfried D. Schubert 

A Solar Radiation Parameterization for Atmospheric Studies 

Ming-Dah Chou and Max J. Suarez 

Filtering Techniques on a Stretched Grid General Circulation 

Model 

Lawrence Takacs, William Sawyer, Max J. Suarez, 

and Michael S. Fox-Rabinowitz 

Atlas of Seasonal Means Simulated by the NSIPP-1 Atmospheric 

GCM 

Julio T. Bacmeister, Philip J. Pegion, Siegfried D. 

Schubert, and Max J. Suarez 

An Assessment of the Predictability of Northern Winter Seasonal 

Means with the NSIPP-1 AGCM 

Philip J. Pegion, Siegfried D. Schubert, and Max J. Suarez 

A Thermal Infrared Radiation Parameterization for Atmospheric 

Studies 

Ming-Dah Chou, Max J. Suarez, Xin-Zhong, Michael M.-H. 

Yan 

The Climate of the FVCCM-3 Model 

Yehui Chang, Siegfried D. Schubert, Shian-Jiann Lin, 

Sharon Nebuda, Bo-Wen Shen 

Design and Implementation of a Parallel Multivariate Ensemble 

Kalman Filter for the Poseidon Ocean General Circulation Model 

Christian L. Keppenne and Michele M. Rienecker 
Volume 22 

August 2002 Ocean Biogeochemical Models 

A Coupled Ocean-Atmosphere Radiative Model for Global 

Watson W. Gregg 

Volume 23 

November 2002 

Prospects for Improved Forecasts of Weather and Short-Term 

Climate Variability on Subseasonal (2-Week to 2-Month) Time 

Scales 

Sigfried D. Schubert, Randall Dole, Huang van den Dool, 

Max J. Suarez, and Duane Waliser 
. I Form Approved REPORT DOCUMENTATION PAGE OMB NO. 0704-0188 

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gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect 01 this 

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1. AGENCY USE ONLY (Leave blank) 3. REPORT TYPE AND DATES COVERED 

Technical Memorandum 

9. SPONSORING I MONITORING AGENCY NAME@) AND ADDRESS (ES) 

14. AND Ternperatuie Data Assimilation witd Salinity 5. FUNDING NUMBERS 

10. SPONSORING / MONITORING 

AGENCY REPORT NUMBER 

Corrections: Validation for the NSIPP Ocean Data Assimilation System 

in the Tropical Pacific Ocean, 1993-1998 

Albert0 Troccoli, Michele M. Rienecker, Christian L. Keppenne, 

Gregory C. Johnson, 

6. AUTHOR@) 

14. SUBJECT TERMS 

NASA Seasonal-to-Interannual Prediction Project (NSIPP) 

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 

OF REPORT OF THIS PAGE OF ABSTRACT 

Unclassified Unclassified Unclassified 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES) 

15. NUMBER OF PAGES 

26 

16. PRICE CODE 

20. LIMITATION OF ABSTRAC 

UL 

Goddard Space Flight Center 

Greenbelt, Maryland 2077 1 

Code 971 

8. PEFORMING ORGANIZATION 

REPORT NUMBER 

2003-024 19-0 

National Aeronautics and Space Administration 

Washington, DC 20546-0001 I TM-2003-104606, Vol. 24 

I 

11. SUPPLEMENTARY NOTES 

Christian L. Keppenne, SAIC, Beltsville, MD; Gregory C. Johnson, NOAA, Seattle, WA 

12a. DISTRIBUTION / AVAILABILITY STATEMENT 

Unclassi fied-Unlimited 

Subject Category: 42 

Report available from the NASA Center for Aerospace Information, 

7121 Standard Drive, Hanover, MD 21076-1320. (301) 621-0390. 

13. ABSTRACT (Maximum 200 words) 

12b. DISTRIBUTION CODE 

The NASA Seasonal-to-Interannual Prediction Project (NSIPP) has developed an Ocean data assimilation system to 

initialize the quasi-isopycnal ocean model used in our experimental coupled-model forecast system. Initial tests of the 

system have focused on the assimilation of temperature profiles in an optimal interpolation framework. It is now recognized that correction of temperature only often introduces spurious water masses. The resulting density distribution 

can be statically unstable and also have a detrimental impact on the velocity distribution. Several simple schemes have 

been developed to try to correct these deficiencies. Here the salinity field is corrected by using a scheme which assumes 

that the temperature-salinity relationship of the model background is preserved during the assimilation. The scheme was 

first introduced for a zlevel model by Troccoli and Haines (1999). A large set of subsurface observations of salinity 

and temperature is used to cross-validate two data assimilation experiments run for the 6-year period 1993-1998. In these 

two experiments only subsurface temperature observations are used, but in one case the salinity field is also updated 

whenever temperature observations are available.