[MITgcm-devel] seaice dynamics in a 2-D set-up

Thu Mar 5 03:44:02 EST 2015

Hi Jean-Michel,

the default for the LSR convergence criterion is this
LSR_ERROR  = 0.0001    _d 0
and that’s what I normally use, I think that now you set it to 2e-9? That’s not what I meant, I suggested to set the parameter SEAICE_EPS = 2e-9; this will make the problem less stiff.

In general, the linear solver converges very slowly, too, but that’s not so important (although I have tried to improve that unsucessufully). What is more important is the convergence of the nonlinear solver (the actual residuals). I suggest that if you have only a limited amount of N iterations to spend, then it makes more sense to have a “large” LSR_ERROR (like the default 1e-4), so that only very few iterations are spent in the linear solver but make more nonlinear iterations (NPSEUDOTIMESTEPS=20 or more). 

There is more that you can try:
 SEAICEuseStrImpCpl = .TRUE. (Hutchings et al 2005)

if your OLx/y > 2, you can try SEAICE_OLx/y = OLx/y - 2, to make the restricted additive Schwarz method, that we use, smoother.

This usually does not help very much, but it’s worth a try.

JFNK: The linear solve for JFNK ist different (FGMRES) and LSR is only the (poor but best available) precondition. I suggest this for practical purposes:

      SEAICEnewtonIterMax = 200,
      SEAICEkrylovIterMax = 50,
      JFNKgamma_nonlin    = 1. _d -05    # default
      JFNKgamma_lin_min   = 0.10 _d 0   # default
      JFNKgamma_lin_max   = 0.99 _d 0   # default
      JFNKres_tFac        = 0.5
      SEAICE_JFNKepsilon  = 1. _d -06 # default
      SEAICE_JFNKalpha    = 1.5, # a bit more agressive than the default

Martin

> On 04 Mar 2015, at 21:23, Jean-Michel Campin <jmc at ocean.mit.edu> wrote:
> 
> Hi Martin,
> 
> Thanks for the suggestions: I increased the tolerance (from 1e-12 before to
> 2e-9 as you suggested) and it reduces the number of "non converging" messages
> that I am getting, also run a little bit faster.
> 
> Now, I am still not sure that the linear solver converges well enough.
> In fact, in this latest run, the report that I am getting from SEAICE_LSR
> seems to indicate no real convergence. For instance:
> SEAICE_LSR: Residual Initial ipass,Uice,Vice=    1  7.743844 19E-03  1.27959541E-02
> SEAICE_LSR (ipass=   1) iters,dU,Resid=    98  1.84258913E-09  7.73824698E-03
> SEAICE_LSR (ipass=   1) iters,dV,Resid=   448  1.99455537E-09  1.27569603E-02
> SEAICE_LSR: Residual Initial ipass,Uice,Vice=    2  7.73904339E-03  1.27591200E-02
> SEAICE_LSR (ipass=   2) iters,dU,Resid=    90  1.91089900E-09  7.73729344E-03
> SEAICE_LSR (ipass=   2) iters,dV,Resid=   114  1.99557763E-09  1.27661753E-02
> the final residual is in fact very close (sometimes smaller, sometimes larger)
> to the initial residual.
> 
> Regarding the JNFK option: I am using the default number of non-linear
> solver iteration (i.e. 2) and I imagined that using JFNK would make sense with
> more Non-Lin iterations (-> increase the cost) but not fixing the linear solver
> convergence.
> 
> And finally regarding Nx=1: since some parts of the solver (the implicit
> tridiagonal part only ?) are not tile independent, I am not sure that
> when I have Nx=1 I really zero out all derivative in X direction.
> I tried SEAICEuseMultiTileSolver=T (but I don't trust too much this
> code because I know who wrote it) but this did not help.
> 
> Cheers,
> Jean-Michel
> 
> On Wed, Mar 04, 2015 at 06:07:27PM +0100, Martin Losch wrote:
>> Hi Jean-Michel,
>> 
>> do you think that Ny=1 is the problem? I don’t think so, because always the LSR solver converges only very slowly. I see the same problem when I start from a horizonally homogeneous ice distribution. It takes a while before the ice starts moving, ie. from uice=vice=0 => solver does not do anything => doesn’t cost anything, but when it starts, LSR picks up, does not converge and uses a lot of time.
>> 
>> If you want a converging (but still expensive) solver use JFNK (for a start the parameters in offline_exf_seaice.dyn_jfnk should be OK, also have a look at SEAICE_OPTIONS.h!)
>> 
>> Also, convergence is always improved for larger seaice_eps (default is 1e-11, but 2e-9 is used very often), and a smoother/better regularization (not implemented: deltaCreg = deltaC + seaice_eps).
>> 
>> Martin
>>> On 04 Mar 2015, at 17:55, Jean-Michel Campin <jmc at ocean.mit.edu> wrote:
>>> 
>>> Hi Martin and others,
>>> 
>>> I am trying to run a simple oceanic 2-D (Y-Z) set-up with seaice dynamics.
>>> It starts without seaice and before seaice forms the LSR solver
>>> converges (not just doing 1 iteration, but within 10-12 iters).
>>> 
>>> But as soon as there is seaice, it stops converging and
>>> more than half of the CPU time is spent in SEAICE_DYNSOLVER
>>> (despite the fact that I have 50 levels + SOM advection scheme).
>>> 
>>> I can reduce the max number of LSR iterations (SOLV_MAX_ITERS),
>>> faster but still not great.
>>> I tried separatly SEAICEuseMultiTileSolver=T and SEAICE_OLx,y=1
>>> but the improvement is very marginal.
>>> 
>>> I am tempting to reduce even more SOLV_MAX_ITERS but start to worry
>>> about what the ice velocity will be.
>>> Is there anything I could try ?
>>> and should we think of some code modification for the specific case
>>> where Nx=1 or Ny=1 ?
>>> 
>>> Cheers,
>>> Jean-Michel
>>> 
>>> _______________________________________________
>>> MITgcm-devel mailing list
>>> MITgcm-devel at mitgcm.org
>>> http://mitgcm.org/mailman/listinfo/mitgcm-devel
>> 
>> 
>> _______________________________________________
>> MITgcm-devel mailing list
>> MITgcm-devel at mitgcm.org
>> http://mitgcm.org/mailman/listinfo/mitgcm-devel
> 
> _______________________________________________
> MITgcm-devel mailing list
> MITgcm-devel at mitgcm.org
> http://mitgcm.org/mailman/listinfo/mitgcm-devel