Skip to content

Commit

Permalink
Follow MOM6 code style guide
Browse files Browse the repository at this point in the history 
* Add ``implicit none ; private`` to this module;
* Put module variables into the control structure for this module;
* Add the description of the units for all real variables;
* Add a consistent two-point indent throughout the module .

TODO:
Without further modifications, adding ``private`` to the control
structure of this module will break the model. Currently, MOM.F90
needs access to ``use_stoch_eos``, ``stanley_coeff``, and some of
the diagnostic ids.
  • Loading branch information
@gustavo-marques @alperaltuntas
gustavo-marques authored and alperaltuntas committed Jul 26, 2022
1 parent 8e833d4 commit 6057714
Showing 1 changed file with 121 additions and 113 deletions.
234 changes: 121 additions & 113 deletions src/core/MOM_stoch_eos.F90
View file
Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
!> Provides the ocean stochastic equation of state
module MOM_stoch_eos

! This file is part of MOM6. See LICENSE.md for the license.
use MOM_grid, only : ocean_grid_type
use MOM_hor_index, only : hor_index_type
Expand All @@ -15,148 +16,156 @@ module MOM_stoch_eos
use MOM_isopycnal_slopes,only : vert_fill_TS
!use random_numbers_mod, only : getRandomNumbers,initializeRandomNumberStream,randomNumberStream

implicit none
implicit none; private
#include <MOM_memory.h>

public MOM_stoch_eos_init
public MOM_stoch_eos_run
public MOM_calc_varT

real,private ALLOCABLE_, dimension(NIMEM_,NJMEM_) :: l2_inv
!< One over sum of the T cell side side lengths squared
real,private ALLOCABLE_, dimension(NIMEM_,NJMEM_) :: rgauss !< nondimensional random Gaussian
real, parameter,private :: tfac=0.27 !< Nondimensional decorrelation time factor, ~1/3.7
real, parameter,private :: amplitude=0.624499 !< Nondimensional std dev of Gaussian
integer ,private :: seed !< PRNG seed
type(PRNG) :: rn_CS !< PRNG control structure

!> Describes parameters of the stochastic component of the EOS
!! correction, described in Stanley et al. JAMES 2020.
type, public :: MOM_stoch_eos_CS
real,public ALLOCABLE_, dimension(NIMEM_,NJMEM_) :: pattern
!< Random pattern for stochastic EOS
real ALLOCABLE_, dimension(NIMEM_,NJMEM_) :: l2_inv
!< One over sum of the T cell side side lengths squared
real ALLOCABLE_, dimension(NIMEM_,NJMEM_) :: rgauss
!< nondimensional random Gaussian
real :: tfac=0.27 !< Nondimensional decorrelation time factor, ~1/3.7
real :: amplitude=0.624499 !< Nondimensional std dev of Gaussian
integer :: seed !< PRNG seed
type(PRNG) :: rn_CS !< PRNG control structure
real ALLOCABLE_, dimension(NIMEM_,NJMEM_) :: pattern
!< Random pattern for stochastic EOS [nondim]
real ALLOCABLE_, dimension(NIMEM_,NJMEM_) :: phi
!< temporal correlation stochastic EOS (deugging)
logical :: use_stoch_eos !< If true, use the stochastic equation of state (Stanley et al. 2020)
real :: stanley_coeff !< Coefficient correlating the temperature gradient
!and SGS T variance; if <0, turn off scheme in all codes
real :: stanley_a !<a in exp(aX) in stochastic coefficient
!< temporal correlation stochastic EOS [nondim]
logical :: use_stoch_eos!< If true, use the stochastic equation of state (Stanley et al. 2020)
real :: stanley_coeff !< Coefficient correlating the temperature gradient
!! and SGS T variance; if <0, turn off scheme in all codes
real :: stanley_a !< a in exp(aX) in stochastic coefficient
real :: kappa_smooth !< A diffusivity for smoothing T/S in vanished layers [Z2 T-1 ~> m2 s-1]

!>@{ Diagnostic IDs
integer :: id_stoch_eos = -1, id_stoch_phi = -1, id_tvar_sgs = -1
!>@}

end type MOM_stoch_eos_CS


contains
subroutine MOM_stoch_eos_init(G,Time,param_file,stoch_eos_CS,restart_CS,diag)
! initialization subroutine called by MOM.F90,
type(param_file_type), intent(in) :: param_file !< structure indicating parameter file to parse
type(ocean_grid_type), intent(in) :: G !< The ocean's grid structure.
type(time_type), intent(in) :: Time !< Time for stochastic process
type(MOM_stoch_eos_CS), intent(inout) :: stoch_eos_CS !< Stochastic control structure
type(MOM_restart_CS), pointer :: restart_CS !< A pointer to the restart control structure.
type(diag_ctrl), target, intent(inout) :: diag !< to control diagnostics

!> Initializes MOM_stoch_eos module.
subroutine MOM_stoch_eos_init(G,Time,param_file,CS,restart_CS,diag)
type(param_file_type), intent(in) :: param_file !< structure indicating parameter file to parse
type(ocean_grid_type), intent(in) :: G !< The ocean's grid structure.
type(time_type), intent(in) :: Time !< Time for stochastic process
type(MOM_stoch_eos_CS), intent(inout) :: CS !< Stochastic control structure
type(MOM_restart_CS), pointer :: restart_CS !< A pointer to the restart control structure.
type(diag_ctrl), target, intent(inout) :: diag !< to control diagnostics

! local variables
integer :: i,j
type(vardesc) :: vd
seed=0
type(vardesc) :: vd
CS%seed=0
! contants
!pi=2*acos(0.0)
call get_param(param_file, "MOM_stoch_eos", "STOCH_EOS", stoch_eos_CS%use_stoch_eos, &
call get_param(param_file, "MOM_stoch_eos", "STOCH_EOS", CS%use_stoch_eos, &
"If true, stochastic perturbations are applied "//&
"to the EOS in the PGF.", default=.false.)
call get_param(param_file, "MOM_stoch_eos", "STANLEY_COEFF", stoch_eos_CS%stanley_coeff, &
call get_param(param_file, "MOM_stoch_eos", "STANLEY_COEFF", CS%stanley_coeff, &
"Coefficient correlating the temperature gradient "//&
"and SGS T variance.", default=-1.0)
call get_param(param_file, "MOM_stoch_eos", "STANLEY_A", stoch_eos_CS%stanley_a, &
call get_param(param_file, "MOM_stoch_eos", "STANLEY_A", CS%stanley_a, &
"Coefficient a which scales chi in stochastic perturbation of the "//&
"SGS T variance.", default=1.0)
call get_param(param_file, "MOM_stoch_eos", "KD_SMOOTH", stoch_eos_CS%kappa_smooth, &
call get_param(param_file, "MOM_stoch_eos", "KD_SMOOTH", CS%kappa_smooth, &
"A diapycnal diffusivity that is used to interpolate "//&
"more sensible values of T & S into thin layers.", &
units="m2 s-1", default=1.0e-6)

!don't run anything if STANLEY_COEFF < 0
if (stoch_eos_CS%stanley_coeff >= 0.0) then
if (CS%stanley_coeff >= 0.0) then

ALLOC_(stoch_eos_CS%pattern(G%isd:G%ied,G%jsd:G%jed)) ; stoch_eos_CS%pattern(:,:) = 0.0
ALLOC_(CS%pattern(G%isd:G%ied,G%jsd:G%jed)) ; CS%pattern(:,:) = 0.0
vd = var_desc("stoch_eos_pattern","nondim","Random pattern for stoch EOS",'h','1')
call register_restart_field(stoch_eos_CS%pattern, vd, .false., restart_CS)
ALLOC_(stoch_eos_CS%phi(G%isd:G%ied,G%jsd:G%jed)) ; stoch_eos_CS%phi(:,:) = 0.0
ALLOC_(l2_inv(G%isd:G%ied,G%jsd:G%jed))
ALLOC_(rgauss(G%isd:G%ied,G%jsd:G%jed))
call get_param(param_file, "MOM_stoch_eos", "SEED_STOCH_EOS", seed, &
call register_restart_field(CS%pattern, vd, .false., restart_CS)
ALLOC_(CS%phi(G%isd:G%ied,G%jsd:G%jed)) ; CS%phi(:,:) = 0.0
ALLOC_(CS%l2_inv(G%isd:G%ied,G%jsd:G%jed))
ALLOC_(CS%rgauss(G%isd:G%ied,G%jsd:G%jed))
call get_param(param_file, "MOM_stoch_eos", "SEED_STOCH_EOS", CS%seed, &
"Specfied seed for random number sequence ", default=0)
call random_2d_constructor(rn_CS, G%HI, Time, seed)
call random_2d_norm(rn_CS, G%HI, rgauss)
call random_2d_constructor(CS%rn_CS, G%HI, Time, CS%seed)
call random_2d_norm(CS%rn_CS, G%HI, CS%rgauss)
! fill array with approximation of grid area needed for decorrelation
! time-scale calculation
do j=G%jsc,G%jec
do i=G%isc,G%iec
l2_inv(i,j)=1.0/(G%dxT(i,j)**2+G%dyT(i,j)**2)
enddo
do i=G%isc,G%iec
CS%l2_inv(i,j)=1.0/(G%dxT(i,j)**2+G%dyT(i,j)**2)
enddo
enddo
if (is_new_run(restart_CS)) then
do j=G%jsc,G%jec
do i=G%isc,G%iec
stoch_eos_CS%pattern(i,j)=amplitude*rgauss(i,j)
enddo
enddo
do j=G%jsc,G%jec
do i=G%isc,G%iec
CS%pattern(i,j)=CS%amplitude*CS%rgauss(i,j)
enddo
enddo
endif

!register diagnostics
stoch_eos_CS%id_tvar_sgs = register_diag_field('ocean_model', 'tvar_sgs', diag%axesTL, Time, &
CS%id_tvar_sgs = register_diag_field('ocean_model', 'tvar_sgs', diag%axesTL, Time, &
'Parameterized SGS Temperature Variance ', 'None')
if (stoch_eos_CS%use_stoch_eos) then
stoch_eos_CS%id_stoch_eos = register_diag_field('ocean_model', 'stoch_eos', diag%axesT1, Time, &
if (CS%use_stoch_eos) then
CS%id_stoch_eos = register_diag_field('ocean_model', 'stoch_eos', diag%axesT1, Time, &
'random pattern for EOS', 'None')
stoch_eos_CS%id_stoch_phi = register_diag_field('ocean_model', 'stoch_phi', diag%axesT1, Time, &
CS%id_stoch_phi = register_diag_field('ocean_model', 'stoch_phi', diag%axesT1, Time, &
'phi for EOS', 'None')
endif
endif

end subroutine MOM_stoch_eos_init
end subroutine MOM_stoch_eos_init

subroutine MOM_stoch_eos_run(G,u,v,delt,Time,stoch_eos_CS,diag)
type(ocean_grid_type), intent(in) :: G !< The ocean's grid structure.
!> Generates a pattern in space and time for the ocean stochastic equation of state
subroutine MOM_stoch_eos_run(G,u,v,delt,Time,CS,diag)
type(ocean_grid_type), intent(in) :: G !< The ocean's grid structure.
real, dimension(SZIB_(G),SZJ_(G),SZK_(G)), &
intent(in) :: u !< The zonal velocity [L T-1 ~> m s-1].
intent(in) :: u !< The zonal velocity [L T-1 ~> m s-1].
real, dimension(SZI_(G),SZJB_(G),SZK_(G)), &
intent(in) :: v !< The meridional velocity [L T-1 ~> m s-1].
real, intent(in) :: delt !< Time step size for AR1 process [T ~> s].
type(time_type), intent(in) :: Time !< Time for stochastic process
type(MOM_stoch_eos_CS), intent(inout) :: stoch_eos_CS !< Stochastic control structure
type(diag_ctrl), target, intent(inout) :: diag !< to control diagnostics
! locals
integer :: i,j
intent(in) :: v !< The meridional velocity [L T-1 ~> m s-1].
real, intent(in) :: delt !< Time step size for AR1 process [T ~> s].
type(time_type), intent(in) :: Time !< Time for stochastic process
type(MOM_stoch_eos_CS), intent(inout) :: CS !< Stochastic control structure
type(diag_ctrl), target, intent(inout) :: diag !< to control diagnostics

! local variables
integer :: i,j
integer :: yr,mo,dy,hr,mn,sc
real :: phi,ubar,vbar
real :: phi,ubar,vbar

call random_2d_constructor(CS%rn_CS, G%HI, Time, CS%seed)
call random_2d_norm(CS%rn_CS, G%HI, CS%rgauss)

call random_2d_constructor(rn_CS, G%HI, Time, seed)
call random_2d_norm(rn_CS, G%HI, rgauss)
! advance AR(1)
do j=G%jsc,G%jec
do i=G%isc,G%iec
ubar=0.5*(u(I,j,1)*G%mask2dCu(I,j)+u(I-1,j,1)*G%mask2dCu(I-1,j))
vbar=0.5*(v(i,J,1)*G%mask2dCv(i,J)+v(i,J-1,1)*G%mask2dCv(i,J-1))
phi=exp(-delt*tfac*sqrt((ubar**2+vbar**2)*l2_inv(i,j)))
stoch_eos_CS%pattern(i,j)=phi*stoch_eos_CS%pattern(i,j) + amplitude*sqrt(1-phi**2)*rgauss(i,j)
stoch_eos_CS%phi(i,j)=phi
enddo
do i=G%isc,G%iec
ubar=0.5*(u(I,j,1)*G%mask2dCu(I,j)+u(I-1,j,1)*G%mask2dCu(I-1,j))
vbar=0.5*(v(i,J,1)*G%mask2dCv(i,J)+v(i,J-1,1)*G%mask2dCv(i,J-1))
phi=exp(-delt*CS%tfac*sqrt((ubar**2+vbar**2)*CS%l2_inv(i,j)))
CS%pattern(i,j)=phi*CS%pattern(i,j) + CS%amplitude*sqrt(1-phi**2)*CS%rgauss(i,j)
CS%phi(i,j)=phi
enddo
enddo

end subroutine MOM_stoch_eos_run
end subroutine MOM_stoch_eos_run

!> Computes a parameterization of the SGS temperature variance
subroutine MOM_calc_varT(G,GV,h,tv,CS,dt)
type(ocean_grid_type), intent(in) :: G !< The ocean's grid structure.
type(verticalGrid_type), intent(in) :: GV !< Vertical grid structure
real, dimension(SZI_(G),SZJ_(G),SZK_(G)), &
intent(in) :: h !< Layer thickness [H ~> m]
type(thermo_var_ptrs), intent(inout) :: tv !< Thermodynamics structure
type(MOM_stoch_eos_CS), intent(inout) :: CS !< Stochastic control structure
real, intent(in) :: dt !< Time increment [T ~> s]

subroutine MOM_calc_varT(G,GV,h,tv,stoch_eos_CS,dt)
type(ocean_grid_type), intent(in) :: G !< The ocean's grid structure.
type(verticalGrid_type), intent(in) :: GV !< Vertical grid structure
real, dimension(SZI_(G),SZJ_(G),SZK_(G)), intent(in) :: h !< Layer thickness [H ~> m]
type(thermo_var_ptrs), intent(inout) :: tv !< Thermodynamics structure
type(MOM_stoch_eos_CS), intent(inout) :: stoch_eos_CS !< Stochastic control structure.
real, intent(in) :: dt !< Time increment [T ~> s]
! locals
! local variables
real, dimension(SZI_(G), SZJ_(G), SZK_(GV)) :: &
T, & !> The temperature (or density) [degC], with the values in
!! in massless layers filled vertically by diffusion.
Expand All @@ -171,42 +180,41 @@ subroutine MOM_calc_varT(G,GV,h,tv,stoch_eos_CS,dt)
! still a poor approximation in the interior when coordinates are strongly tilted.
if (.not. associated(tv%varT)) call safe_alloc_ptr(tv%varT, G%isd, G%ied, G%jsd, G%jed, GV%ke)

call vert_fill_TS(h, tv%T, tv%S, stoch_eos_CS%kappa_smooth*dt, T, S, G, GV, halo_here=1, larger_h_denom=.true.)
call vert_fill_TS(h, tv%T, tv%S, CS%kappa_smooth*dt, T, S, G, GV, halo_here=1, larger_h_denom=.true.)

do k=1,G%ke
do j=G%jsc,G%jec
do i=G%isc,G%iec
hl(1) = h(i,j,k) * G%mask2dT(i,j)
hl(2) = h(i-1,j,k) * G%mask2dCu(I-1,j)
hl(3) = h(i+1,j,k) * G%mask2dCu(I,j)
hl(4) = h(i,j-1,k) * G%mask2dCv(i,J-1)
hl(5) = h(i,j+1,k) * G%mask2dCv(i,J)

! SGS variance in i-direction [degC2]
dTdi2 = ( ( G%mask2dCu(I ,j) * G%IdxCu(I ,j) * ( T(i+1,j,k) - T(i,j,k) ) &
+ G%mask2dCu(I-1,j) * G%IdxCu(I-1,j) * ( T(i,j,k) - T(i-1,j,k) ) &
) * G%dxT(i,j) * 0.5 )**2
! SGS variance in j-direction [degC2]
dTdj2 = ( ( G%mask2dCv(i,J ) * G%IdyCv(i,J ) * ( T(i,j+1,k) - T(i,j,k) ) &
+ G%mask2dCv(i,J-1) * G%IdyCv(i,J-1) * ( T(i,j,k) - T(i,j-1,k) ) &
) * G%dyT(i,j) * 0.5 )**2
tv%varT(i,j,k) = stoch_eos_CS%stanley_coeff * ( dTdi2 + dTdj2 )
! Turn off scheme near land
tv%varT(i,j,k) = tv%varT(i,j,k) * (minval(hl) / (maxval(hl) + GV%H_subroundoff))
enddo
enddo
do j=G%jsc,G%jec
do i=G%isc,G%iec
hl(1) = h(i,j,k) * G%mask2dT(i,j)
hl(2) = h(i-1,j,k) * G%mask2dCu(I-1,j)
hl(3) = h(i+1,j,k) * G%mask2dCu(I,j)
hl(4) = h(i,j-1,k) * G%mask2dCv(i,J-1)
hl(5) = h(i,j+1,k) * G%mask2dCv(i,J)

! SGS variance in i-direction [degC2]
dTdi2 = ( ( G%mask2dCu(I ,j) * G%IdxCu(I ,j) * ( T(i+1,j,k) - T(i,j,k) ) &
+ G%mask2dCu(I-1,j) * G%IdxCu(I-1,j) * ( T(i,j,k) - T(i-1,j,k) ) &
) * G%dxT(i,j) * 0.5 )**2
! SGS variance in j-direction [degC2]
dTdj2 = ( ( G%mask2dCv(i,J ) * G%IdyCv(i,J ) * ( T(i,j+1,k) - T(i,j,k) ) &
+ G%mask2dCv(i,J-1) * G%IdyCv(i,J-1) * ( T(i,j,k) - T(i,j-1,k) ) &
) * G%dyT(i,j) * 0.5 )**2
tv%varT(i,j,k) = CS%stanley_coeff * ( dTdi2 + dTdj2 )
! Turn off scheme near land
tv%varT(i,j,k) = tv%varT(i,j,k) * (minval(hl) / (maxval(hl) + GV%H_subroundoff))
enddo
enddo
enddo
! if stochastic, perturb
if (stoch_eos_CS%use_stoch_eos) then
do k=1,G%ke
do j=G%jsc,G%jec
do i=G%isc,G%iec
tv%varT(i,j,k) = exp (stoch_eos_CS%stanley_a * stoch_eos_CS%pattern(i,j)) * tv%varT(i,j,k)
enddo
if (CS%use_stoch_eos) then
do k=1,G%ke
do j=G%jsc,G%jec
do i=G%isc,G%iec
tv%varT(i,j,k) = exp (CS%stanley_a * CS%pattern(i,j)) * tv%varT(i,j,k)
enddo
enddo
enddo
enddo
endif
end subroutine MOM_calc_varT
end subroutine MOM_calc_varT

end module MOM_stoch_eos

0 comments on commit 6057714

Please sign in to comment.