# 4.11. Global Ocean State Estimation¶

(in directory: verification/tutorial_global_oce_optim/)

## 4.11.1. Overview¶

This experiment illustrates the optimization capacity of the MITgcm: here, a high level description.

In this tutorial, a very simple case is used to illustrate the optimization capacity of the MITgcm. Using an ocean configuration with realistic geography and bathymetry on a \(4\times4^\circ\) spherical polar grid, we estimate a time-independent surface heat flux adjustment \(Q_\mathrm{netm}\) that attempts to bring the model climatology into consistency with observations (Levitus and Boyer (1994a,b) [LB94a][LB94b]).

This adjustment \(Q_\mathrm{netm}\) (a 2-D field only function of longitude and latitude) is the control variable of an optimization problem. It is inferred by an iterative procedure using an ‘adjoint technique’ and a least-squares method (see, for example, Stammer et al. (2002) [SWG+02] and Ferriera et a. (2005) [FMH05].

The ocean model is run forward in time and the quality of the solution is determined by a cost function, \(J_1\), a measure of the departure of the model climatology from observations:

where \(\overline{T}_i\) and \(\overline{T}_i^{lev}\) are,
respectively, the model and observed potential temperature at each grid
point \(i\). The differences are weighted by an *a priori*
uncertainty \(\sigma_i^T\) on observations (as provided by
Levitus and Boyer (1994a)
[LB94a]). The error \(\sigma_i^T\) is only a
function of depth and varies from 0.5 K at the surface to 0.05 K at the
bottom of the ocean, mainly reflecting the decreasing temperature
variance with depth (see Figure 4.47a). A value of \(J_1\) of order 1
means that the model is, on average, within observational uncertainties.

The cost function also places constraints on the adjustment to insure it is “reasonable”, i.e., of order of the uncertainties on the observed surface heat flux:

where \(\sigma^Q_i\) are the *a priori* errors on the observed heat
flux as estimated by Stammer et al. (2002) [SWG+02] from 30% of local
root-mean-square variability of the NCEP forcing field (see Figure 4.47b).

The total cost function is defined as \(J=\lambda_1 J_1+ \lambda_2 J_2\) where \(\lambda_1\) and \(\lambda_2\) are weights controlling the relative contribution of the two components. The adjoint model then yields the sensitivities \(\partial J/\partial Q_\mathrm{netm}\) of \(J\) relative to the 2-D fields \(Q_\mathrm{netm}\). Using a line-searching algorithm (Gilbert and Lemaréchal 1989 [GLemarechal89]), \(Q_\mathrm{netm}\) is adjusted then in the sense to reduce \(J\) — the procedure is repeated until convergence.

Figure 4.48 shows the results of such an optimization. The model is
started from rest and from January-mean temperature and salinity initial
conditions taken from the Levitus dataset. The experiment is run a year
and the averaged temperature over the whole run (i.e., annual mean) is
used in the cost function (4.58) to evaluate the model 1.
Only the top 2 levels are used. The first guess \(Q_\mathrm{netm}\)
is chosen to be zero. The weights \(\lambda_1\) and
\(\lambda_2\) are set to 1 and 2, respectively. The total cost
function converges after 15 iterations, decreasing from 6.1 to 2.7 (the
temperature contribution decreases from 6.1 to 1.8 while the heat flux
one increases from 0 to 0.42). The right panels of Figure 4.48
illustrate the evolution of the temperature error at the surface from
iteration 0 to iteration 15. Unsurprisingly, the largest errors at
iteration 0 (up to 6 ^{o}C, top left panels) are found in
the Western boundary currents. After optimization, the departure of the
model temperature from observations is reduced to 1 ^{o}C
or less almost everywhere except in the Pacific equatorial cold tongue.
Comparison of the initial temperature error (top, right) and heat flux
adjustment (bottom, left) shows that the system basically increased the
heat flux out of the ocean where temperatures were too warm and
vice-versa. Obviously, heat flux uncertainties are not solely
responsible for temperature errors, and the heat flux adjustment partly
compensates the poor representation of narrow currents (Western boundary
currents, equatorial currents) at \(4\times4^\circ\) resolution.
This is allowed by the large *a priori* error on the heat flux Figure 4.47.
The Pacific cold tongue is a counter example: there, heat
fluxes uncertainties are fairly small (about 20 W m^{-2}), and a
large temperature errors remains after optimization.

## 4.11.2. Implementation of the control variable and the cost function¶

One of the goals of this tutorial is to illustrate how to implement a new
control variable. Most of this is fairly generic and is done in pkg/ctrl
and pkg/cost. The modifications can be
tracked by the CPP option ALLOW_HFLUXM_CONTROL or the comment
`cHFLUXM_CONTROL`

. The more specific modifications required for the
experiment are found in
verification/tutorial_global_oce_optim/code_ad. Here follows a brief
description of the implementation.

### 4.11.2.1. The control variable¶

The adjustment \(Q_\mathrm{netm}\) is activated by setting `#define`

ALLOW_HFLUXM_CONTROL in code_ad/CTRL_OPTIONS.h.

It is first implemented as a “normal” forcing variable. It is defined in model/inc/FFIELDS.h, initialized to zero in model/src/ini_forcing.F, and then used in model/src/external_forcing_surf.F. \(Q_\mathrm{netm}\) is made a control variable in pkg/ctrl by modifying the following subroutines:

pkg/ctrl/ctrl_init.F where \(Q_\mathrm{netm}\) is defined as the control variable number 24,

pkg/ctrl/ctrl_pack.F which writes, at the end of each iteration, the sensitivity of the cost function \(\partial J/\partial Q_\mathrm{netm}\) in to a file to be used by the line-search algorithm,

pkg/ctrl/ctrl_unpack.F which reads, at the start of each iteration, the updated adjustment as provided by the line-search algorithm,

pkg/ctrl/ctrl_map_forcing.F in which the updated adjustment is added to the first guess \(Q_\mathrm{netm}\).

Note also some minor changes in pkg/ctrl/ctrl.h, pkg/ctrl/ctrl_readparms.F, and pkg/ctrl/ctrl_dummy.h (xx_hfluxm_file, fname_hfluxm, xx_hfluxm_dummy).

### 4.11.2.2. Cost functions¶

The cost functions are implemented using pkg/cost.

The temperature cost function \(J_1\) which measures the drift of the mean model temperature from the Levitus climatology is implemented in /verification/tutorial_global_oce_optim/code_ad/cost_temp.F. It is activated by

`#define`

ALLOW_COST_TEMP in code_ad/COST_OPTIONS.h. It requires the mean temperature of the model which is obtained by accumulating the temperature in pkg/cost/cost_tile.F (called at each time step). The value of the cost function is stored in objf_temp and its weight \(\lambda_1\) in mult_temp.The heat flux cost function, penalizing the departure of the surface heat flux from observations is implemented in /verification/tutorial_global_oce_optim/code_ad/cost_hflux.F, and activated by

`#define`

ALLOW_COST_HFLUXM in code_ad/COST_OPTIONS.h. The value of the cost function is stored in objf_hfluxm and its weight \(\lambda_2\) in mult_hflux.The subroutine pkg/cost/cost_final.F calls the cost function subroutines and makes the (weighted) sum of the various contributions.

The various weights used in the cost functions are read in /verification/tutorial_global_oce_optim/code_ad/cost_weights.F. The weight of the cost functions are read in pkg/cost/cost_readparms.F from the input file verification/tutorial_global_oce_optim/input_ad/data.cost.

## 4.11.3. Code Configuration¶

The experiment files in verification/tutorial_global_oce_optim/code_ad/ and verification/tutorial_global_oce_optim/input_ad/ contain the code customizations and parameter settings. Most of them are identical to those used in the Global Ocean ( experiment verification/tutorial_global_oce_latlon/). Below, we describe some of the customizations required for this experiment.

### 4.11.3.1. Compilation-time customizations in code_ad¶

`#define`

ALLOW_ECCO_OPTIMIZATION

### 4.11.3.2. Running-time customizations in input_ad¶

input_ad/data: note the smaller cg2dTargetResidual than in the forward-only experiment,

input_ad/data.optim specifies the iteration number,

input_ad/data.ctrl is used, in particular, to specify the name of the sensitivity and adjustment files associated to a control variable,

input_ad/data.cost: parameters of the cost functions, in particular lastinterval specifies the length of time-averaging for the model temperature to be used in the cost function (4.58),

input_ad/data.pkg: note that the Gradient Check package is turned on by default (useGrdchk

`=.TRUE.`

),`Err_hflux.bin`

and`Err_levitus_15layer.bin`

are the files containing the heat flux and potential temperature uncertainties, respectively.

## 4.11.4. Compiling¶

The optimization experiment requires two executables: 1) the MITgcm and
its adjoint (`mitgcmuv_ad`

) and 2) the line-search algorithm
(`optim.x`

).

### 4.11.4.1. Compilation of MITgcm and its adjoint: `mitcgmuv_ad`

¶

Before compiling, first note that in the directory code_ad, two files must be updated:

code_ad/code_ad_diff.list which lists new subroutines to be compiled by the TAF software (code_ad/cost_temp.F and code_ad/cost_hflux.F),

the file code_ad/ad_optfile.local provides a list of the control variables and the name of cost function to the TAF software.

Then, in the directory build, type:

```
% ../../../tools/genmake2 -mods=../code_ad -adof=../code_ad/ad_optfile.local
% make depend
% make adall
```

to generate the MITgcm executable `mitgcmuv_ad`

.

### 4.11.4.2. Compilation of the line-search algorithm: `optim.x`

¶

This is done from the directories lsopt/ and optim/ (found in the top MITgcm directory). In
lsopt/, unzip the `blash1`

library adapted to your platform (see lsopt/README), and change
the `Makefile`

accordingly. Compile with:

```
% make all
```

(more details in lsopt/lsopt_doc.txt)

In optim/, the path of the directory where `mitgcm_ad`

was compiled
must be specified in the `Makefile`

in the variable INCLUDEDIRS. The file
name of the control variable (here, xx_hfluxm_file) must be added to
the namelist read by optim/optim_numbmod.F. Then use

```
% make depend
```

and

```
% make
```

to generate the line-search executable `optim.x`

.

## 4.11.5. Running the estimation¶

Make a new subdirectory `input_ad/OPTIM`

.
Copy the `mitgcmuv_ad`

executable to input_ad
and `optim.x`

to this subdirectory.
`cd`

into input_ad/. The first iteration
is somewhat particular and is best done “by hand” while the following
iterations can be run automatically (see below). Check that the
iteration number is set to 0 in input_ad/data.optim and run MITgcm:

```
% ./mitgcmuv_ad
```

The output files `adxx_hfluxm.0000000000.*`

and `xx_hfluxm.0000000000.*`

contain the sensitivity of the cost function to \(Q_\mathrm{netm}\)
and the adjustment to \(Q_\mathrm{netm}\) (zero at the first
iteration), respectively. Two other files called
`costhflux_tut_MITgcm.opt0000`

and `ctrlhflux_tut_MITgcm.opt0000`

are
also generated. They essentially contain the same information as the
`adxx_.hfluxm*`

and `xx_hfluxm*`

files, but in a compressed format.
These two files are the only ones involved in the communication between
the adjoint model `mitgcmuv_ad`

and the line-search algorithm
`optim.x`

. Only at the first iteration, are they both generated by
`mitgcmuv_ad`

. Subsequently, `costhflux_tut_MITgcm.opt`

\(n\) is
an output of the adjoint model at iteration \(n\) and an input of
the line-search. The latter returns an updated adjustment in
`ctrlhflux_tut_MITgcm.opt`

\(n+1\) to be used as an input of the
adjoint model at iteration \(n+1\).

At the first iteration, move `costhflux_tut_MITgcm.opt0000`

and
`ctrlhflux_tut_MITgcm.opt0000`

to `input_ad/OPTIM`

,
move into this directory and link input_ad/data.optim
and input_ad/data.ctrl locally:

```
% cd OPTIM/
% ln -s ../data.optim .
% ln -s ../data.ctrl .
```

The target cost function fmin needs to be specified in input_ad/data.optim: as a rule of thumb, it should be about 0.95-0.90 times the value of the cost function at the first iteration. This value is only used at the first iteration and does not need to be updated afterward. However, it implicitly specifies the “pace” at which the cost function is going down (if you are lucky and it does indeed diminish!).

Once this is done, run the line-search algorithm:

```
% ./optim.x
```

which computes the updated adjustment for iteration 1,
`ctrlhflux_tut_MITgcm.opt0001`

.

The following iterations can be executed automatically using the shell
script input_ad/cycsh. This script will take care of
changing the iteration numbers in input_ad/data.optim, launch the adjoint
model, clean and store the outputs, move the `costhflux*`

and `ctrlhflux*`

files, and run the line-search algorithm. Edit input_ad/cycsh to specify the
prefix of the directories used to store the outputs and the maximum
number of iteration.

- 1
Because of the daily automatic testing, the experiment as found in the repository is set-up with a very small number of time-steps. To reproduce the results shown here, one needs to set nTimeSteps = 360 and lastinterval =31104000 (both corresponding to a year, see Section 4.11.3.2 for further details).