From dcece0a9622d1cbc65021ecfe17be2133c122a44 Mon Sep 17 00:00:00 2001
From: Stefania <stefania.ciliberti@cmcc.it>
Date: Sat, 12 Feb 2022 15:35:47 +0100
Subject: [PATCH] add ADIAB_WAVE test
---
tests/ADIAB_WAVE/.DS_Store | Bin 0 -> 6148 bytes
tests/ADIAB_WAVE/EXPREF/axis_def_nemo.xml | 1 +
tests/ADIAB_WAVE/EXPREF/context_nemo.xml | 37 ++
tests/ADIAB_WAVE/EXPREF/domain_def_nemo.xml | 1 +
.../ADIAB_WAVE/EXPREF/field_def_nemo-oce.xml | 1 +
tests/ADIAB_WAVE/EXPREF/file_def_nemo-oce.xml | 20 +
tests/ADIAB_WAVE/EXPREF/grid_def_nemo.xml | 1 +
tests/ADIAB_WAVE/EXPREF/iodef.xml | 26 +
tests/ADIAB_WAVE/EXPREF/namelist_cfg | 451 ++++++++++++++
tests/ADIAB_WAVE/EXPREF/namelist_ref | 1 +
tests/ADIAB_WAVE/MY_SRC/sbcwave.F90 | 563 ++++++++++++++++++
tests/ADIAB_WAVE/MY_SRC/usrdef_hgr.F90 | 115 ++++
tests/ADIAB_WAVE/MY_SRC/usrdef_istate.F90 | 91 +++
tests/ADIAB_WAVE/MY_SRC/usrdef_nam.F90 | 112 ++++
tests/ADIAB_WAVE/MY_SRC/usrdef_sbc.F90 | 95 +++
tests/ADIAB_WAVE/MY_SRC/usrdef_zgr.F90 | 275 +++++++++
tests/ADIAB_WAVE/README.md | 55 ++
tests/ADIAB_WAVE/cpp_ADIAB_WAVE.fcm | 1 +
.../input_data/bdyT_u2dE_y0001m01d01.nc | Bin 0 -> 740 bytes
.../input_data/bdyT_u2dW_y0001m01d01.nc | Bin 0 -> 740 bytes
.../input_data/bdyU_u2dE_y0001m01d01.nc | Bin 0 -> 748 bytes
.../input_data/bdyU_u2dW_y0001m01d01.nc | Bin 0 -> 824 bytes
.../input_data/bdyV_u2dE_y0001m01d01.nc | Bin 0 -> 752 bytes
.../input_data/bdyV_u2dW_y0001m01d01.nc | Bin 0 -> 752 bytes
.../sdw_WW3_ADIABWAVE_y0001m01d01.nc.tar.gz | Bin 0 -> 5795024 bytes
.../notebook/adiabwave_notebook.ipynb | 288 +++++++++
26 files changed, 2134 insertions(+)
create mode 100644 tests/ADIAB_WAVE/.DS_Store
create mode 120000 tests/ADIAB_WAVE/EXPREF/axis_def_nemo.xml
create mode 100644 tests/ADIAB_WAVE/EXPREF/context_nemo.xml
create mode 120000 tests/ADIAB_WAVE/EXPREF/domain_def_nemo.xml
create mode 120000 tests/ADIAB_WAVE/EXPREF/field_def_nemo-oce.xml
create mode 100644 tests/ADIAB_WAVE/EXPREF/file_def_nemo-oce.xml
create mode 120000 tests/ADIAB_WAVE/EXPREF/grid_def_nemo.xml
create mode 100644 tests/ADIAB_WAVE/EXPREF/iodef.xml
create mode 100644 tests/ADIAB_WAVE/EXPREF/namelist_cfg
create mode 120000 tests/ADIAB_WAVE/EXPREF/namelist_ref
create mode 100644 tests/ADIAB_WAVE/MY_SRC/sbcwave.F90
create mode 100644 tests/ADIAB_WAVE/MY_SRC/usrdef_hgr.F90
create mode 100644 tests/ADIAB_WAVE/MY_SRC/usrdef_istate.F90
create mode 100644 tests/ADIAB_WAVE/MY_SRC/usrdef_nam.F90
create mode 100644 tests/ADIAB_WAVE/MY_SRC/usrdef_sbc.F90
create mode 100644 tests/ADIAB_WAVE/MY_SRC/usrdef_zgr.F90
create mode 100644 tests/ADIAB_WAVE/README.md
create mode 100644 tests/ADIAB_WAVE/cpp_ADIAB_WAVE.fcm
create mode 100644 tests/ADIAB_WAVE/input_data/bdyT_u2dE_y0001m01d01.nc
create mode 100644 tests/ADIAB_WAVE/input_data/bdyT_u2dW_y0001m01d01.nc
create mode 100644 tests/ADIAB_WAVE/input_data/bdyU_u2dE_y0001m01d01.nc
create mode 100644 tests/ADIAB_WAVE/input_data/bdyU_u2dW_y0001m01d01.nc
create mode 100644 tests/ADIAB_WAVE/input_data/bdyV_u2dE_y0001m01d01.nc
create mode 100644 tests/ADIAB_WAVE/input_data/bdyV_u2dW_y0001m01d01.nc
create mode 100644 tests/ADIAB_WAVE/input_data/sdw_WW3_ADIABWAVE_y0001m01d01.nc.tar.gz
create mode 100644 tests/ADIAB_WAVE/notebook/adiabwave_notebook.ipynb
diff --git a/tests/ADIAB_WAVE/.DS_Store b/tests/ADIAB_WAVE/.DS_Store
new file mode 100644
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diff --git a/tests/ADIAB_WAVE/EXPREF/axis_def_nemo.xml b/tests/ADIAB_WAVE/EXPREF/axis_def_nemo.xml
new file mode 120000
index 000000000..6117f35ba
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/axis_def_nemo.xml
@@ -0,0 +1 @@
+../../../cfgs/SHARED/axis_def_nemo.xml
\ No newline at end of file
diff --git a/tests/ADIAB_WAVE/EXPREF/context_nemo.xml b/tests/ADIAB_WAVE/EXPREF/context_nemo.xml
new file mode 100644
index 000000000..c7598d423
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/context_nemo.xml
@@ -0,0 +1,37 @@
+<!--
+ ==============================================================================================
+ NEMO context
+==============================================================================================
+-->
+<context id="nemo">
+ <!-- $id$ -->
+ <variable_definition>
+ <!-- Year/Month/Day of time origin for NetCDF files; defaults to 1800-01-01 -->
+ <variable id="ref_year" type="int"> 1900 </variable>
+ <variable id="ref_month" type="int"> 01 </variable>
+ <variable id="ref_day" type="int"> 01 </variable>
+ <variable id="rho0" type="float" > 1026.0 </variable>
+ <variable id="cpocean" type="float" > 3991.86795711963 </variable>
+ <variable id="convSpsu" type="float" > 0.99530670233846 </variable>
+ <variable id="rhoic" type="float" > 917.0 </variable>
+ <variable id="rhosn" type="float" > 330.0 </variable>
+ <variable id="missval" type="float" > 1.e20 </variable>
+ </variable_definition>
+
+<!-- Fields definition -->
+ <field_definition src="./field_def_nemo-oce.xml"/> <!-- NEMO ocean dynamics -->
+
+<!-- Files definition -->
+ <file_definition src="./file_def_nemo-oce.xml"/> <!-- NEMO ocean dynamics -->
+
+<!-- Axis definition -->
+ <axis_definition src="./axis_def_nemo.xml"/>
+
+<!-- Domain definition -->
+ <domain_definition src="./domain_def_nemo.xml"/>
+
+<!-- Grids definition -->
+ <grid_definition src="./grid_def_nemo.xml"/>
+
+
+</context>
diff --git a/tests/ADIAB_WAVE/EXPREF/domain_def_nemo.xml b/tests/ADIAB_WAVE/EXPREF/domain_def_nemo.xml
new file mode 120000
index 000000000..f344125af
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/domain_def_nemo.xml
@@ -0,0 +1 @@
+../../../cfgs/SHARED/domain_def_nemo.xml
\ No newline at end of file
diff --git a/tests/ADIAB_WAVE/EXPREF/field_def_nemo-oce.xml b/tests/ADIAB_WAVE/EXPREF/field_def_nemo-oce.xml
new file mode 120000
index 000000000..ff9706813
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/field_def_nemo-oce.xml
@@ -0,0 +1 @@
+../../../cfgs/SHARED/field_def_nemo-oce.xml
\ No newline at end of file
diff --git a/tests/ADIAB_WAVE/EXPREF/file_def_nemo-oce.xml b/tests/ADIAB_WAVE/EXPREF/file_def_nemo-oce.xml
new file mode 100644
index 000000000..3edf4bc05
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/file_def_nemo-oce.xml
@@ -0,0 +1,20 @@
+<?xml version="1.0"?>
+ <!--
+============================================================================================================
+= output files definition =
+= Define your own files =
+= put the variables you want... =
+============================================================================================================
+ -->
+
+ <file_definition type="one_file" name="@expname@" sync_freq="10d" min_digits="4">
+ <file_group id="1s" output_freq="1s" output_level="10" enabled=".TRUE." > <!-- 5d files -->
+ <file id="file1" name_suffix="_grid_U" description="ocean U grid variables" >
+ <field field_ref="e3u" name="e3u_inst" long_name="instantaneous U-cell thickness" operation="instant" />
+ <field field_ref="uoce" name="uo_inst" long_name="instantaneous sea water x velocity" operation="instant" />
+ <field field_ref="ustokes" name="sdzocrtx" long_name="instantaneous sea water sd x velocity" operation="instant"/>
+ </file>
+
+ </file_group>
+
+ </file_definition>
diff --git a/tests/ADIAB_WAVE/EXPREF/grid_def_nemo.xml b/tests/ADIAB_WAVE/EXPREF/grid_def_nemo.xml
new file mode 120000
index 000000000..1be74edf6
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/grid_def_nemo.xml
@@ -0,0 +1 @@
+../../../cfgs/SHARED/grid_def_nemo.xml
\ No newline at end of file
diff --git a/tests/ADIAB_WAVE/EXPREF/iodef.xml b/tests/ADIAB_WAVE/EXPREF/iodef.xml
new file mode 100644
index 000000000..b49154cdd
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/iodef.xml
@@ -0,0 +1,26 @@
+<?xml version="1.0"?>
+<simulation>
+
+<!-- ============================================================================================ -->
+<!-- XIOS context -->
+<!-- ============================================================================================ -->
+
+ <context id="xios" >
+
+ <variable_definition>
+
+ <variable id="info_level" type="int">10</variable>
+ <variable id="using_server" type="bool">true</variable>
+ <variable id="using_oasis" type="bool">false</variable>
+ <variable id="oasis_codes_id" type="string" >oceanx</variable>
+
+ </variable_definition>
+ </context>
+
+<!-- ============================================================================================ -->
+<!-- NEMO CONTEXT add and suppress the components you need -->
+<!-- ============================================================================================ -->
+
+ <context id="nemo" src="./context_nemo.xml"/> <!-- NEMO -->
+
+</simulation>
diff --git a/tests/ADIAB_WAVE/EXPREF/namelist_cfg b/tests/ADIAB_WAVE/EXPREF/namelist_cfg
new file mode 100644
index 000000000..1fffb700c
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/namelist_cfg
@@ -0,0 +1,451 @@
+!!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+!! NEMO/OCE Configuration namelist : overwrite default values defined in SHARED/namelist_ref
+!!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+!! OVERFLOW configuration !!
+!!======================================================================
+!! *** Domain & Run management namelists *** !!
+!! !!
+!! namrun parameters of the run
+!! namdom space and time domain
+!! namcfg parameters of the configuration (default: user defined GYRE)
+!! namwad Wetting and drying (default: OFF)
+!! namtsd data: temperature & salinity (default: OFF)
+!! namcrs coarsened grid (for outputs and/or TOP) (ln_crs =T)
+!! namc1d 1D configuration options ("key_c1d")
+!! namc1d_dyndmp 1D newtonian damping applied on currents ("key_c1d")
+!! namc1d_uvd 1D data (currents) ("key_c1d")
+!!======================================================================
+!
+!-----------------------------------------------------------------------
+&namusr_def ! User defined : ADIAB_WAVE configuration
+!-----------------------------------------------------------------------
+ ! ! type of vertical coordinate
+ ln_zco = .false. ! z-coordinate
+ ln_zps = .false. ! z-partial-step coordinate
+ ln_sco = .true. ! s-coordinate
+ rn_dx = 10. ! horizontal resolution [meters]
+ rn_dy = 10.
+ rn_dz = 0.05 ! vertical resolution [meters]
+ ln_STOKES_ADIAB = .true. ! Stokes Drift (Shallow/Intermediate water)
+/
+!-----------------------------------------------------------------------
+&namrun ! parameters of the run
+!-----------------------------------------------------------------------
+ nn_no = 0 ! job number (no more used...)
+ cn_exp ="ADIAB_GLM"! experience name
+ nn_it000 = 1 ! first time step
+ nn_itend = 900 ! here 1h of simulation (=6120 time-step)
+ nn_date0 = 010101 ! date at nit_0000 (format yyyymmdd) used if ln_rstart=F or (ln_rstart=T and nn_rstctl=0 or 1)
+ nn_time0 = 0001 ! initial time of day in hhmm
+ nn_istate = 1 ! output the initial state (1) or not (0)
+ nn_stock = 3600 ! frequency of creation of a restart file (modulo referenced to 1)
+ nn_write = 1 ! frequency of write in the output file (modulo referenced to nn_it000)
+/
+!-----------------------------------------------------------------------
+&namcfg ! parameters of the configuration
+!-----------------------------------------------------------------------
+ ln_read_cfg = .false. ! (=T) read the domain configuration file
+ ! ! (=F) user defined configuration ==>>> see usrdef(_...) modules
+ ln_write_cfg = .true. ! (=T) create the domain configuration file
+ cn_domcfg_out = "domain_cfg_out" ! newly created domain configuration filename
+/
+!-----------------------------------------------------------------------
+&namdom ! time and space domain
+!-----------------------------------------------------------------------
+ ln_linssh = .true. ! =T linear free surface ==>> model level are fixed in time
+ !
+ rn_Dt = 1. !time step for the dynamics (and tracer if nn_acc=0)
+ rn_atfp = 0. ! asselin time filter parameter
+/
+!-----------------------------------------------------------------------
+&namtsd ! Temperature & Salinity Data (init/dmp) (default: OFF)
+!-----------------------------------------------------------------------
+/
+!!======================================================================
+!! *** Surface Boundary Condition namelists *** !!
+!! !!
+!! namsbc surface boundary condition manager (default: NO selection)
+!! namsbc_flx flux formulation (ln_flx =T)
+!! namsbc_blk Bulk formulae formulation (ln_blk =T)
+!! namsbc_cpl CouPLed formulation ("key_oasis3" )
+!! namsbc_sas Stand-Alone Surface module (SAS_SRC only)
+!! namsbc_iif Ice-IF: use observed ice cover (nn_ice = 1 )
+!! namtra_qsr penetrative solar radiation (ln_traqsr =T)
+!! namsbc_ssr sea surface restoring term (for T and/or S) (ln_ssr =T)
+!! namsbc_rnf river runoffs (ln_rnf =T)
+!! namsbc_apr Atmospheric Pressure (ln_apr_dyn =T)
+!! namsbc_isf ice shelf melting/freezing (ln_isfcav =T : read (ln_read_cfg=T) or set or usr_def_zgr )
+!! namsbc_iscpl coupling option between land ice model and ocean (ln_isfcav =T)
+!! namsbc_wave external fields from wave model (ln_wave =T)
+!! namberg iceberg floats (ln_icebergs=T)
+!!======================================================================
+!
+!-----------------------------------------------------------------------
+&namsbc ! Surface Boundary Condition manager (default: NO selection)
+!-----------------------------------------------------------------------
+ nn_fsbc = 1. ! frequency of surface boundary condition computation
+ ! (also = the frequency of sea-ice & iceberg model call)
+ ln_usr = .true. ! user defined formulation (T => check usrdef_sbc)
+ ln_blk = .false. ! Bulk formulation (T => fill namsbc_blk )
+ nn_ice = 0 ! =0 no ice boundary condition
+ ln_traqsr = .false. ! Light penetration in the ocean (T => fill namtra_qsr )
+ ln_rnf = .false. ! runoffs (T => fill namsbc_rnf)
+ ln_ssr = .false. ! Sea Surface Restoring on T and/or S (T => fill namsbc_ssr)
+ nn_fwb = 0 ! FreshWater Budget: =0 unchecked
+ ln_wave = .true.
+/
+!-----------------------------------------------------------------------
+&namsbc_wave ! External fields from wave model (ln_wave=T)
+!-----------------------------------------------------------------------
+ ln_sdw = .true. ! get the 2D Surf Stokes Drift & Compute the 3D stokes drift
+ ln_stcor = .false. ! add Stokes Coriolis and tracer advection terms
+ ln_cdgw = .false. ! Neutral drag coefficient read from wave model
+ ln_tauoc = .false. ! ocean stress is modified by wave induced stress
+ ln_wave_test= .false. ! Test case with constant wave fields
+!
+ ln_charn = .false. ! Charnock coefficient read from wave model (IFS only)
+ ln_taw = .false. ! ocean stress is modified by wave induced stress (coupled mode)
+ ln_phioc = .false. ! TKE flux from wave model
+ ln_bern_srfc= .false. ! wave induced pressure. Bernoulli head J term
+ ln_breivikFV_2016 = .false. ! breivik 2016 vertical stokes profile
+ ln_vortex_force = .false. ! Vortex Force term
+ ln_stshear = .false. ! include stokes shear in EKE computation
+
+!
+ cn_dir = '../input_data/' ! root directory for the waves data location
+ !___________!_________________________!___________________!___________!_____________!________!___________!__________________!__________!_______________!
+ ! file name !freq(hrs)! var ! time int ! clim ! 'yrly'/ ! weights filename ! rotation ! land/sea mask !
+ ! ! ! (if <0 months) ! name ! (logical) ! (T/F) ! 'monthly' ! ! pairing ! filename !
+ sn_usd = 'sdw_WW3_ADIABWAVE' , 0.00027778 , 'uuss' , .true. , .false., 'daily' , '' , '' , ''
+ sn_vsd = 'sdw_WW3_ADIABWAVE' , 0.00027778 , 'vuss' , .true. , .false., 'daily' , '' , '' , ''
+ sn_hsw = 'sdw_WW3_ADIABWAVE' , 0.00027778 , 'hs' , .true. , .false., 'daily' , '' , '' , ''
+ sn_wmp = 'sdw_WW3_ADIABWAVE' , 0.00027778 , 't0m1' , .true. ,.false. , 'daily' , '' , '' , ''
+ sn_wnum = 'sdw_WW3_ADIABWAVE' , 0.00027778 , 'wnum' , .true. ,.false. , 'daily' , '' , '' , ''
+/
+!!======================================================================
+!! *** Lateral boundary condition *** !!
+!! !!
+!! namlbc lateral momentum boundary condition (default: NO selection)
+!! namagrif agrif nested grid (read by child model only) ("key_agrif")
+!! nam_tide Tidal forcing (default: OFF)
+!! nambdy Unstructured open boundaries (default: OFF)
+!! nambdy_dta Unstructured open boundaries - external data (see nambdy)
+!! nambdy_tide tidal forcing at open boundaries (default: OFF)
+!!======================================================================
+!
+!-----------------------------------------------------------------------
+&namlbc ! lateral momentum boundary condition (default: NO selection)
+!-----------------------------------------------------------------------
+ rn_shlat = 3. ! free slip
+/
+!-----------------------------------------------------------------------
+&nambdy ! unstructured open boundaries (default: OFF)
+!-----------------------------------------------------------------------
+ ln_bdy = .true. ! Use unstructured open boundaries
+ nb_bdy = 2 ! number of open boundary sets
+ ln_coords_file = .false., .false. ! =T : read bdy coordinates from file
+ cn_coords_file = 'coordinates.bdy.nc' ! bdy coordinates files
+ ln_mask_file = .false. ! =T : read mask from file
+ cn_mask_file = '' ! name of mask file (if ln_mask_file=.TRUE.)
+ cn_dyn2d = 'flather' , 'flather' !
+ nn_dyn2d_dta = 1, 1 ! = 0, bdy data are equal to the initial state
+ ! ! = 1, bdy data are read in 'bdydata .nc' files
+ ! ! = 2, use tidal harmonic forcing data from files
+ ! ! = 3, use external data AND tidal harmonic forcing
+ cn_dyn3d = 'none','none' !
+ nn_dyn3d_dta = 0, 0 ! = 0, bdy data are equal to the initial state
+ ! ! = 1, bdy data are read in 'bdydata .nc' files
+ cn_tra = 'frs','frs' !
+ nn_tra_dta = 0, 0 ! = 0, bdy data are equal to the initial state
+ ! ! = 1, bdy data are read in 'bdydata .nc' files
+ cn_ice = 'none', 'none' !
+ nn_ice_dta = 0 , 0 ! = 0, bdy data are equal to the initial state
+ ! ! = 1, bdy data are read in 'bdydata .nc' files
+ cn_ice = 'none', 'none' !
+ nn_ice_dta = 0 , 0 ! = 0, bdy data are equal to the initial state
+ ! ! = 1, bdy data are read in 'bdydata .nc' files
+ !
+ ln_tra_dmp =.false., .false. ! open boudaries conditions for tracers
+ ln_dyn3d_dmp =.false., .false. ! open boundary condition for baroclinic velocities
+ rn_time_dmp = 0.01 ! Damping time scale in days
+ rn_time_dmp_out = 0.01 ! Outflow damping time scale
+ nn_rimwidth = 1, 1 ! width of the relaxation zone
+ ln_vol = .false. ! total volume correction (see nn_volctl parameter)
+ nn_volctl = 0 ! = 0, the total water flux across open boundaries is zero
+/
+!-----------------------------------------------------------------------
+&nambdy_index
+!-----------------------------------------------------------------------
+ ctypebdy ='W'
+ nbdyind = 2
+ nbdybeg = 1
+ nbdyend = 5
+/
+!-----------------------------------------------------------------------
+&nambdy_index
+!-----------------------------------------------------------------------
+ ctypebdy = 'E'
+ nbdyind = 76
+ nbdybeg = 1
+ nbdyend = 5
+/
+!-----------------------------------------------------------------------
+&nambdy_dta ! open boundaries - external data (see nam_bdy)
+!-----------------------------------------------------------------------
+ ln_zinterp = .false. ! T if a vertical interpolation is required. Variables gdep[tuv] and e3[tuv] must exist in the file
+ ln_full_vel = .false. ! T if [uv]3d are "full" velocities and not only its baroclinic components
+ cn_dir = '../input_data/' ! root directory for the BDY data location
+ !___________!_________________________!___________________!___________!_____________!________!___________!__________________!__________!_______________!
+ ! ! file name ! frequency (hours) ! variable ! time interp.! clim ! 'yearly'/ ! weights filename ! rotation ! land/sea mask !
+ ! ! ! (if <0 months) ! name ! (logical) ! (T/F) ! 'monthly' ! ! pairing ! filename !
+ bn_ssh = 'bdyT_u2dW' , 24 , 'sossheig' , .false. , .false. , 'daily' , '' , '' , ''
+ bn_u2d = 'bdyU_u2dW' , 24 , 'vobtcrtx' , .false. , .false. , 'daily' , '' , '' , ''
+ bn_v2d = 'bdyV_u2dW' , 24 , 'vobtcrty' , .false. , .false. , 'daily' , '' , '' , ''
+/
+!-----------------------------------------------------------------------
+&nambdy_dta ! open boundaries - external data (see nam_bdy)
+!-----------------------------------------------------------------------
+ ln_zinterp = .false. ! T if a vertical interpolation is required. Variables gdep[tuv] and e3[tuv] must exist in the file
+ ln_full_vel = .false. ! T if [uv]3d are "full" velocities and not only its baroclinic components
+ cn_dir = '../input_data/' ! root directory for the BDY data location
+ !___________!_________________________!___________________!___________!_____________!________!___________!__________________!__________!_______________!
+ ! ! file name ! frequency (hours) ! variable ! time interp.! clim ! 'yearly'/ ! weights filename ! rotation ! land/sea mask !
+ ! ! ! (if <0 months) ! name ! (logical) ! (T/F) ! 'monthly' ! ! pairing ! filename !
+ bn_ssh = 'bdyT_u2dE' , 24 , 'sossheig' , .false. , .false. , 'daily' , '' , '' , ''
+ bn_u2d = 'bdyU_u2dE' , 24 , 'vobtcrtx' , .false. , .false. , 'daily' , '' , '' , ''
+ bn_v2d = 'bdyV_u2dE' , 24 , 'vobtcrty' , .false. , .false. , 'daily' , '' , '' , ''
+/
+!!======================================================================
+!! *** Top/Bottom boundary condition *** !!
+!! !!
+!! namdrg top/bottom drag coefficient (default: NO selection)
+!! namdrg_top top friction (ln_drg_OFF=F & ln_isfcav=T)
+!! namdrg_bot bottom friction (ln_drg_OFF=F)
+!! nambbc bottom temperature boundary condition (default: OFF)
+!! nambbl bottom boundary layer scheme (default: OFF)
+!!======================================================================
+!
+!-----------------------------------------------------------------------
+&namdrg ! top/bottom drag coefficient (default: NO selection)
+!-----------------------------------------------------------------------
+ ln_drg_OFF = .true. ! free-slip : Cd = 0 (F => fill namdrg_bot
+ ln_lin = .false. ! linear drag: Cd = Cd0 Uc0 & namdrg_top)
+ ln_non_lin = .false. ! non-linear drag: Cd = Cd0 |U|
+ ln_loglayer= .false. ! logarithmic drag: Cd = vkarmn/log(z/z0) |U|
+ !
+ ln_drgimp = .false. ! implicit top/bottom friction flag
+/
+!-----------------------------------------------------------------------
+&namdrg_bot ! BOTTOM friction (ln_drg_OFF =F)
+!-----------------------------------------------------------------------
+ rn_Cd0 = 0.e-3 ! drag coefficient [-]
+ rn_Uc0 = 0. ! ref. velocity [m/s] (linear drag=Cd0*Uc0)
+ rn_Cdmax = 0. ! drag value maximum [-] (logarithmic drag)
+ rn_ke0 = 0.e-3 ! background kinetic energy [m2/s2] (non-linear cases)
+ rn_z0 = 0.e-3 ! roughness [m] (ln_loglayer=T)
+ ln_boost = .false. ! =T regional boost of Cd0 ; =F constant
+ rn_boost = 0. ! local boost factor [-]
+/
+!!======================================================================
+!! Tracer (T-S) namelists !!
+!! !!
+!! nameos equation of state (default: NO selection)
+!! namtra_adv advection scheme (default: NO selection)
+!! namtra_ldf lateral diffusion scheme (default: NO selection)
+!! namtra_mle mixed layer eddy param. (Fox-Kemper param.) (default: OFF)
+!! namtra_eiv eddy induced velocity param. (default: OFF)
+!! namtra_dmp T & S newtonian damping (default: OFF)
+!!======================================================================
+!
+!-----------------------------------------------------------------------
+&nameos ! ocean Equation Of Seawater (default: NO selection)
+!-----------------------------------------------------------------------
+ ln_seos = .true. ! = Use simplified equation of state (S-EOS)
+ ! ! rd(T,S,Z)*rho0 = -a0*(1+.5*lambda*dT+mu*Z+nu*dS)*dT+b0*dS
+ rn_a0 = 0.2 ! thermal expension coefficient (for simplified equation of state)
+ rn_b0 = 0. ! saline expension coefficient (for simplified equation of state)
+ rn_lambda1 = 0. ! cabbeling coeff in T^2 (=0 for linear eos)
+ rn_lambda2 = 0. ! cabbeling coeff in S^2 (=0 for linear eos)
+ rn_mu1 = 0. ! thermobaric coeff. in T (=0 for linear eos)
+ rn_mu2 = 0. ! thermobaric coeff. in S (=0 for linear eos)
+ rn_nu = 0. ! cabbeling coeff in T*S (=0 for linear eos)
+/
+!-----------------------------------------------------------------------
+&namtra_adv ! advection scheme for tracer (default: NO selection)
+!-----------------------------------------------------------------------
+ ln_traadv_cen = .false. ! 2nd order centered scheme
+ nn_cen_h = 4 ! =2/4, horizontal 2nd order CEN / 4th order CEN
+ nn_cen_v = 4 ! =2/4, vertical 2nd order CEN / 4th order COMPACT
+ ln_traadv_fct = .true. ! FCT scheme
+ nn_fct_h = 4 ! =2/4, horizontal 2nd / 4th order
+ nn_fct_v = 4 ! =2/4, vertical 2nd / COMPACT 4th order
+ ln_traadv_mus = .false. ! MUSCL scheme
+ ln_mus_ups = .false. ! use upstream scheme near river mouths
+ ln_traadv_ubs = .false. ! UBS scheme
+ nn_ubs_v = 2 ! =2 , vertical 2nd order FCT / COMPACT 4th order
+ ln_traadv_qck = .false. ! QUICKEST scheme
+/
+!-----------------------------------------------------------------------
+&namtra_ldf ! lateral diffusion scheme for tracers (default: NO selection)
+!-----------------------------------------------------------------------
+ ! ! Operator type: both false = No lateral diffusion
+ ln_traldf_OFF = .true. ! No explicit diffusion
+ ln_traldf_lap = .false. ! laplacian operator
+ ln_traldf_blp = .false. ! bilaplacian operator
+/
+!!======================================================================
+!! *** Dynamics namelists *** !!
+!! !!
+!! nam_vvl vertical coordinate options (default: z-star)
+!! namdyn_adv formulation of the momentum advection (default: NO selection)
+!! namdyn_vor advection scheme (default: NO selection)
+!! namdyn_hpg hydrostatic pressure gradient (default: NO selection)
+!! namdyn_spg surface pressure gradient (default: NO selection)
+!! namdyn_ldf lateral diffusion scheme (default: NO selection)
+!! namdta_dyn offline TOP: dynamics read in files (OFF_SRC only)
+!!======================================================================
+!
+!-----------------------------------------------------------------------
+&nam_vvl ! vertical coordinate options (default: zstar)
+!-----------------------------------------------------------------------
+ ln_vvl_zstar = .true. ! zstar vertical coordinate
+/
+!-----------------------------------------------------------------------
+&namdyn_adv ! formulation of the momentum advection (default: NO selection)
+!-----------------------------------------------------------------------
+ ln_dynadv_OFF = .false. ! linear dynamics (no momentum advection)
+ ln_dynadv_vec = .false. ! vector form (T) or flux form (F)
+ nn_dynkeg = 0 ! scheme for grad(KE): =0 C2 ; =1 Hollingsworth correction
+ ln_dynadv_cen2 = .false. ! flux form - 2nd order centered scheme
+ ln_dynadv_ubs = .true. ! flux form - 3rd order UBS scheme
+/
+!-----------------------------------------------------------------------
+&namdyn_vor ! Vorticity / Coriolis scheme (default: NO selection)
+!-----------------------------------------------------------------------
+ ln_dynvor_ene = .true. ! enstrophy conserving scheme
+ ln_dynvor_ens = .false. ! energy conserving scheme
+ ln_dynvor_mix = .false. ! mixed scheme
+ ln_dynvor_een = .false. ! energy & enstrophy scheme
+/
+!-----------------------------------------------------------------------
+&namdyn_hpg ! Hydrostatic pressure gradient option (default: NO selection)
+!-----------------------------------------------------------------------
+ ln_hpg_zps = .false. ! z-coordinate - partial steps (interpolation)
+ ln_hpg_sco = .true. ! s-coordinate (standard jacobian formulation)
+/
+!-----------------------------------------------------------------------
+&namdyn_spg ! surface pressure gradient (default: NO selection)
+!-----------------------------------------------------------------------
+ ln_dynspg_ts = .true. ! split-explicit free surface
+ ln_bt_fw = .true. ! Forward integration of barotropic Eqs.
+ ln_bt_av = .true. ! Time filtering of barotropic variables
+ nn_bt_flt = 1 ! Time filter choice = 0 None
+ ! ! = 1 Boxcar over nn_e sub-steps
+ ! ! = 2 Boxcar over 2*nn_e " "
+ ln_bt_auto = .true. ! Number of sub-step defined from:
+ nn_e = 1 ! =F : the number of sub-step in rn_Dt seconds
+/
+!-----------------------------------------------------------------------
+&namdyn_ldf ! lateral diffusion on momentum (default: NO selection)
+!-----------------------------------------------------------------------
+ ! ! Type of the operator :
+ ln_dynldf_OFF = .true. ! No operator (i.e. no explicit diffusion)
+ ln_dynldf_lap = .false. ! laplacian operator
+ ln_dynldf_blp = .false. ! bilaplacian operator
+ ! ! Direction of action :
+ ln_dynldf_lev = .false. ! iso-level
+ ln_dynldf_hor = .false. ! horizontal (geopotential)
+ ln_dynldf_iso = .false. ! iso-neutral
+ ! ! Coefficient
+ nn_ahm_ijk_t = 0 ! space/time variation of eddy coef
+ ! ! = 0 constant
+ ! ! time invariant coefficients : ahm = 1/2 Uv*Lv (lap case)
+ ! ! bhm = 1/12 Uv*Lv^3 (blp case)
+ rn_Uv = 2.0 ! lateral viscous velocity [m/s] (nn_ahm_ijk_t= 0, 10, 20, 30)
+ rn_Lv = 1000. ! lateral viscous length [m] (nn_ahm_ijk_t= 0, 10)
+/
+!!======================================================================
+!! vertical physics namelists !!
+!! !!
+!! namzdf vertical physics manager (default: NO selection)
+!! namzdf_ric richardson number vertical mixing (ln_zdfric=T)
+!! namzdf_tke TKE vertical mixing (ln_zdftke=T)
+!! namzdf_gls GLS vertical mixing (ln_zdfgls=T)
+!! namzdf_osm OSM vertical diffusion (ln_zdfosm=T)
+!! namzdf_iwm tidal mixing parameterization (ln_zdfiwm=T)
+!!======================================================================
+!
+!-----------------------------------------------------------------------
+&namzdf ! vertical physics manager (default: NO selection)
+!-----------------------------------------------------------------------
+ ! ! adaptive-implicit vertical advection
+ ln_zad_Aimp = .false. ! Courant number dependent scheme (Shchepetkin 2015)
+ !
+ ! ! type of vertical closure (required)
+ ln_zdfcst = .true. ! constant mixing
+ ln_zdfric = .false. ! local Richardson dependent formulation (T => fill namzdf_ric)
+ ln_zdftke = .false. ! Turbulent Kinetic Energy closure (T => fill namzdf_tke)
+ ln_zdfgls = .false. ! Generic Length Scale closure (T => fill namzdf_gls)
+ !
+ ! ! convection
+ ln_zdfevd = .false. ! enhanced vertical diffusion
+ nn_evdm = 0 ! apply on tracer (=0) or on tracer and momentum (=1)
+ rn_evd = 100. ! mixing coefficient [m2/s]
+ ln_zdfnpc = .false. ! Non-Penetrative Convective algorithm
+ nn_npc = 1 ! frequency of application of npc
+ nn_npcp = 365 ! npc control print frequency
+ !
+ ln_zdfddm = .false. ! double diffusive mixing
+ rn_avts = 1.e-4 ! maximum avs (vertical mixing on salinity)
+ rn_hsbfr = 1.6 ! heat/salt buoyancy flux ratio
+ !
+ ! ! gravity wave-driven vertical mixing
+ ln_zdfiwm = .false. ! internal wave-induced mixing (T => fill namzdf_iwm)
+ ln_zdfswm = .false. ! surface wave-induced mixing (T => ln_wave=ln_sdw=T )
+ !
+ ! ! coefficients
+ rn_avm0 = 0.e-4 ! vertical eddy viscosity [m2/s] (background Kz if ln_zdfcst=F)
+ rn_avt0 = 0.0 ! vertical eddy diffusivity [m2/s] (background Kz if ln_zdfcst=F)
+ nn_avb = 0 ! profile for background avt & avm (=1) or not (=0)
+ nn_havtb = 0 ! horizontal shape for avtb (=1) or not (=0)
+/
+!!======================================================================
+!! *** Diagnostics namelists *** !!
+!! !!
+!! namtrd dynamics and/or tracer trends (default: OFF)
+!! namhsb Heat and salt budgets (default: OFF)
+!! namdiu Cool skin and warm layer models (default: OFF)
+!! namdiu Cool skin and warm layer models (default: OFF)
+!! namflo float parameters (default: OFF)
+!! nam_diadct transports through some sections (default: OFF)
+!! nam_dia25h 25h Mean Output (default: OFF)
+!! namnc4 netcdf4 chunking and compression settings ("key_netcdf4")
+!!======================================================================
+!
+!!======================================================================
+!! *** Observation & Assimilation *** !!
+!! !!
+!! namobs observation and model comparison (default: OFF)
+!! nam_asminc assimilation increments ('key_asminc')
+!!======================================================================
+!
+!!======================================================================
+!! *** Miscellaneous namelists *** !!
+!! !!
+!! nammpp Massively Parallel Processing ("key_mpp_mpi")
+!! namctl Control prints (default: OFF)
+!! namsto Stochastic parametrization of EOS (default: OFF)
+!!======================================================================
+!
+!-----------------------------------------------------------------------
+&nammpp ! Massively Parallel Processing ("key_mpp_mpi)
+!-----------------------------------------------------------------------
+/
+!-----------------------------------------------------------------------
+&namctl ! Control prints (default: OFF)
+!-----------------------------------------------------------------------
+/
+!-----------------------------------------------------------------------
+&namsto ! Stochastic parametrization of EOS (default: OFF)
+!-----------------------------------------------------------------------
+/
diff --git a/tests/ADIAB_WAVE/EXPREF/namelist_ref b/tests/ADIAB_WAVE/EXPREF/namelist_ref
new file mode 120000
index 000000000..976828637
--- /dev/null
+++ b/tests/ADIAB_WAVE/EXPREF/namelist_ref
@@ -0,0 +1 @@
+../../../cfgs/SHARED/namelist_ref
\ No newline at end of file
diff --git a/tests/ADIAB_WAVE/MY_SRC/sbcwave.F90 b/tests/ADIAB_WAVE/MY_SRC/sbcwave.F90
new file mode 100644
index 000000000..070d04706
--- /dev/null
+++ b/tests/ADIAB_WAVE/MY_SRC/sbcwave.F90
@@ -0,0 +1,563 @@
+MODULE sbcwave
+ !!======================================================================
+ !! *** MODULE sbcwave ***
+ !! Wave module
+ !!======================================================================
+ !! History : 3.3 ! 2011-09 (M. Adani) Original code: Drag Coefficient
+ !! : 3.4 ! 2012-10 (M. Adani) Stokes Drift
+ !! 3.6 ! 2014-09 (E. Clementi,P. Oddo) New Stokes Drift Computation
+ !! - ! 2016-12 (G. Madec, E. Clementi) update Stoke drift computation
+ !! + add sbc_wave_ini routine
+ !! 4.2 ! 2020-12 (G. Madec, E. Clementi) updates, new Stoke drift computation
+ !! according to Couvelard et al.,2019
+ !!----------------------------------------------------------------------
+
+ !!----------------------------------------------------------------------
+ !! sbc_stokes : calculate 3D Stokes-drift velocities
+ !! sbc_wave : wave data from wave model: forced (netcdf files) or coupled mode
+ !! sbc_wave_init : initialisation fo surface waves
+ !!----------------------------------------------------------------------
+ USE phycst ! physical constants
+ USE oce ! ocean variables
+ USE dom_oce ! ocean domain variables
+ USE sbc_oce ! Surface boundary condition: ocean fields
+ USE bdy_oce ! open boundary condition variables
+ USE domvvl ! domain: variable volume layers
+ USE usrdef_nam , ONLY: ln_STOKES_ADIAB
+ !
+ USE iom ! I/O manager library
+ USE in_out_manager ! I/O manager
+ USE lib_mpp ! distribued memory computing library
+ USE fldread ! read input fields
+ IMPLICIT NONE
+ PRIVATE
+
+ PUBLIC sbc_stokes ! routine called in sbccpl
+ PUBLIC sbc_wave ! routine called in sbcmod
+ PUBLIC sbc_wave_init ! routine called in sbcmod
+
+ ! Variables checking if the wave parameters are coupled (if not, they are read from file)
+ LOGICAL, PUBLIC :: cpl_hsig = .FALSE.
+ LOGICAL, PUBLIC :: cpl_phioc = .FALSE.
+ LOGICAL, PUBLIC :: cpl_sdrftx = .FALSE.
+ LOGICAL, PUBLIC :: cpl_sdrfty = .FALSE.
+ LOGICAL, PUBLIC :: cpl_wper = .FALSE.
+ LOGICAL, PUBLIC :: cpl_wnum = .FALSE.
+ LOGICAL, PUBLIC :: cpl_wstrf = .FALSE.
+ LOGICAL, PUBLIC :: cpl_wdrag = .FALSE.
+ LOGICAL, PUBLIC :: cpl_charn = .FALSE.
+ LOGICAL, PUBLIC :: cpl_taw = .FALSE.
+ LOGICAL, PUBLIC :: cpl_bhd = .FALSE.
+ LOGICAL, PUBLIC :: cpl_tusd = .FALSE.
+ LOGICAL, PUBLIC :: cpl_tvsd = .FALSE.
+
+ INTEGER :: jpfld ! number of files to read for stokes drift
+ INTEGER :: jp_usd ! index of stokes drift (i-component) (m/s) at T-point
+ INTEGER :: jp_vsd ! index of stokes drift (j-component) (m/s) at T-point
+ INTEGER :: jp_hsw ! index of significant wave hight (m) at T-point
+ INTEGER :: jp_wmp ! index of mean wave period (s) at T-point
+
+ TYPE(FLD), ALLOCATABLE, DIMENSION(:) :: sf_cd ! structure of input fields (file informations, fields read) Drag Coefficient
+ TYPE(FLD), ALLOCATABLE, DIMENSION(:) :: sf_sd ! structure of input fields (file informations, fields read) Stokes Drift
+ TYPE(FLD), ALLOCATABLE, DIMENSION(:) :: sf_wn ! structure of input fields (file informations, fields read) wave number for Qiao
+ TYPE(FLD), ALLOCATABLE, DIMENSION(:) :: sf_tauoc ! structure of input fields (file informations, fields read) normalized wave stress into the ocean
+
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: cdn_wave !: Neutral drag coefficient at t-point
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: hsw !: Significant Wave Height at t-point
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: wmp !: Wave Mean Period at t-point
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: wnum !: Wave Number at t-point
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: tauoc_wave !: stress reduction factor at t-point
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: tsd2d !: Surface Stokes Drift module at t-point
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: div_sd !: barotropic stokes drift divergence
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: ut0sd, vt0sd !: surface Stokes drift velocities at t-point
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:,:) :: usd, vsd, wsd !: Stokes drift velocities at u-, v- & w-points, resp.u
+!
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: charn !: charnock coefficient at t-point
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: tawx !: Net wave-supported stress, u
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: tawy !: Net wave-supported stress, v
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: twox !: wave-ocean momentum flux, u
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: twoy !: wave-ocean momentum flux, v
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: tauoc_wavex !: stress reduction factor at, u component
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: tauoc_wavey !: stress reduction factor at, v component
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: phioc !: tke flux from wave model
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: KZN2 !: Kz*N2
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: bhd_wave !: Bernoulli head. wave induce pression
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:) :: tusd, tvsd !: Stokes drift transport
+ REAL(wp), PUBLIC, ALLOCATABLE, DIMENSION(:,:,:) :: ZMX !: Kz*N2
+ !! * Substitutions
+# include "do_loop_substitute.h90"
+# include "domzgr_substitute.h90"
+ !!----------------------------------------------------------------------
+ !! NEMO/OCE 4.0 , NEMO Consortium (2018)
+ !! $Id: sbcwave.F90 14433 2021-02-11 08:06:49Z smasson $
+ !! Software governed by the CeCILL license (see ./LICENSE)
+ !!----------------------------------------------------------------------
+CONTAINS
+
+ SUBROUTINE sbc_stokes( Kmm )
+ !!---------------------------------------------------------------------
+ !! *** ROUTINE sbc_stokes ***
+ !!
+ !! ** Purpose : compute the 3d Stokes Drift according to Breivik et al.,
+ !! 2014 (DOI: 10.1175/JPO-D-14-0020.1)
+ !!
+ !! ** Method : - Calculate the horizontal Stokes drift velocity (Breivik et al. 2014)
+ !! - Calculate its horizontal divergence
+ !! - Calculate the vertical Stokes drift velocity
+ !! - Calculate the barotropic Stokes drift divergence
+ !!
+ !! ** action : - tsd2d : module of the surface Stokes drift velocity
+ !! - usd, vsd, wsd : 3 components of the Stokes drift velocity
+ !! - div_sd : barotropic Stokes drift divergence
+ !!---------------------------------------------------------------------
+ INTEGER, INTENT(in) :: Kmm ! ocean time level index
+ INTEGER :: jj, ji, jk ! dummy loop argument
+ INTEGER :: ik ! local integer
+ REAL(wp) :: ztransp, zfac, ztemp, zsp0, zsqrt, zbreiv16_w
+ REAL(wp) :: zdep_u, zdep_v, zkh_u, zkh_v, zda_u, zda_v, sdtrp
+ REAL(wp) :: zkd_u, zkd_v, zdep_ue, zdep_ve ! variable for Stokes drift in SHALLOW/INTER WATER
+ REAL(wp), DIMENSION(:,:) , ALLOCATABLE :: zk_t, zk_u, zk_v, zu0_sd, zv0_sd ! 2D workspace
+ REAL(wp), DIMENSION(:,:,:), ALLOCATABLE :: ze3divh, zInt_w ! 3D workspace
+ !!---------------------------------------------------------------------
+ !
+ ALLOCATE( ze3divh(jpi,jpj,jpkm1) ) ! jpkm1 -> avoid lbc_lnk on jpk that is not defined
+ ALLOCATE( zInt_w(jpi,jpj,jpk) )
+ ALLOCATE( zk_t(jpi,jpj), zk_u(jpi,jpj), zk_v(jpi,jpj), zu0_sd(jpi,jpj), zv0_sd(jpi,jpj) )
+ zk_t (:,:) = 0._wp
+ zk_u (:,:) = 0._wp
+ zk_v (:,:) = 0._wp
+ zu0_sd (:,:) = 0._wp
+ zv0_sd (:,:) = 0._wp
+ ze3divh (:,:,:) = 0._wp
+
+ !
+ ! select parameterization for the calculation of vertical Stokes drift
+ ! exp. wave number at t-point
+ IF( ln_breivikFV_2016 ) THEN
+ ! Assumptions : ut0sd and vt0sd are surface Stokes drift at T-points
+ ! sdtrp is the norm of Stokes transport
+ !
+ zfac = 0.166666666667_wp
+ DO_2D( 1, 1, 1, 1 ) ! In the deep-water limit we have ke = ||ust0||/( 6 * ||transport|| )
+ zsp0 = SQRT( ut0sd(ji,jj)*ut0sd(ji,jj) + vt0sd(ji,jj)*vt0sd(ji,jj) ) !<-- norm of Surface Stokes drift
+ tsd2d(ji,jj) = zsp0
+ IF( cpl_tusd .AND. cpl_tvsd ) THEN !stokes transport is provided in coupled mode
+ sdtrp = SQRT( tusd(ji,jj)*tusd(ji,jj) + tvsd(ji,jj)*tvsd(ji,jj) ) !<-- norm of Surface Stokes drift transport
+ ELSE
+ ! Stokes drift transport estimated from Hs and Tmean
+ sdtrp = 2.0_wp * rpi / 16.0_wp * &
+ & hsw(ji,jj)*hsw(ji,jj) / MAX( wmp(ji,jj), 0.0000001_wp )
+ ENDIF
+ zk_t (ji,jj) = zfac * zsp0 / MAX ( sdtrp, 0.0000001_wp ) !<-- ke = ||ust0||/( 6 * ||transport|| )
+ END_2D
+ !# define zInt_w ze3divh
+ DO_3D( 1, 1, 1, 1, 1, jpk ) ! Compute the primitive of Breivik 2016 function at W-points
+ zfac = - 2._wp * zk_t (ji,jj) * gdepw(ji,jj,jk,Kmm) !<-- zfac should be negative definite
+ ztemp = EXP ( zfac )
+ zsqrt = SQRT( -zfac )
+ zbreiv16_w = ztemp - SQRT(rpi)*zsqrt*ERFC(zsqrt) !Eq. 16 Breivik 2016
+ zInt_w(ji,jj,jk) = ztemp - 4._wp * zk_t (ji,jj) * gdepw(ji,jj,jk,Kmm) * zbreiv16_w
+ END_3D
+!
+ DO jk = 1, jpkm1
+ zfac = 0.166666666667_wp
+ DO_2D( 1, 1, 1, 1 ) !++ Compute the FV Breivik 2016 function at T-points
+ zsp0 = zfac / MAX(zk_t (ji,jj),0.0000001_wp)
+ ztemp = zInt_w(ji,jj,jk) - zInt_w(ji,jj,jk+1)
+ zu0_sd(ji,jj) = ut0sd(ji,jj) * zsp0 * ztemp * tmask(ji,jj,jk)
+ zv0_sd(ji,jj) = vt0sd(ji,jj) * zsp0 * ztemp * tmask(ji,jj,jk)
+ END_2D
+ DO_2D( 1, 0, 1, 0 ) ! ++ Interpolate at U/V points
+ zfac = 1.0_wp / e3u(ji ,jj,jk,Kmm)
+ usd(ji,jj,jk) = 0.5_wp * zfac * ( zu0_sd(ji,jj)+zu0_sd(ji+1,jj) ) * umask(ji,jj,jk)
+ zfac = 1.0_wp / e3v(ji ,jj,jk,Kmm)
+ vsd(ji,jj,jk) = 0.5_wp * zfac * ( zv0_sd(ji,jj)+zv0_sd(ji,jj+1) ) * vmask(ji,jj,jk)
+ END_2D
+ ENDDO
+ !# undef zInt_w
+ !
+ ELSE IF (ln_STOKES_ADIAB) THEN
+ DO_2D( 1, 0, 1, 0 )
+ ! velocity at u- & v-points
+ zk_u(ji,jj) = 0.5_wp * ( wnum(ji,jj) + wnum(ji+1,jj) )
+ zk_v(ji,jj) = 0.5_wp * ( wnum(ji,jj) + wnum(ji,jj+1) )
+ !
+ zu0_sd(ji,jj) = 0.5_wp * ( ut0sd(ji,jj) + ut0sd(ji+1,jj) )
+ zv0_sd(ji,jj) = 0.5_wp * ( vt0sd(ji,jj) + vt0sd(ji,jj+1) )
+ END_2D
+
+ ! !== horizontal Stokes Drift 3D velocity ==!
+ DO_3D( 0, 0, 0, 0, 1, jpkm1 )
+ zdep_u = 0.5_wp * ( gdept(ji,jj,jk,Kmm) + gdept(ji+1,jj,jk,Kmm) )
+ zdep_v = 0.5_wp * ( gdept(ji,jj,jk,Kmm) + gdept(ji,jj+1,jk,Kmm) )
+ zdep_ue= 0.5_wp * ( gdept(ji,jj,jpkm1,Kmm) + gdept(ji+1,jj,jpkm1,Kmm) )
+ zdep_ve= 0.5_wp * ( gdept(ji,jj,jpkm1,Kmm) + gdept(ji+1,jj,jpkm1,Kmm) )
+ !
+ zkh_u = zk_u(ji,jj) * zdep_u ! k * depth
+ zkh_v = zk_v(ji,jj) * zdep_v
+ zkd_u = zk_u(ji,jj) * zdep_ue
+ zkd_v = zk_v(ji,jj) * zdep_ve
+ ! Depth attenuation
+ zda_u = COSH(-2.0_wp*zkh_u+2.0_wp*zkd_u)/COSH(2.0_wp*zkd_u)
+ zda_v = COSH(-2.0_wp*zkh_v+2.0_wp*zkd_v)/COSH(2.0_wp*zkd_v)
+ !
+ usd(ji,jj,jk) = zda_u * zu0_sd(ji,jj) * umask(ji,jj,jk)
+ vsd(ji,jj,jk) = zda_v * zv0_sd(ji,jj) * vmask(ji,jj,jk)
+ END_3D
+ ELSE
+ zfac = 2.0_wp * rpi / 16.0_wp
+ DO_2D( 1, 1, 1, 1 )
+ ! Stokes drift velocity estimated from Hs and Tmean
+ ztransp = zfac * hsw(ji,jj)*hsw(ji,jj) / MAX( wmp(ji,jj), 0.0000001_wp )
+ ! Stokes surface speed
+ tsd2d(ji,jj) = SQRT( ut0sd(ji,jj)*ut0sd(ji,jj) + vt0sd(ji,jj)*vt0sd(ji,jj))
+ ! Wavenumber scale
+ zk_t(ji,jj) = ABS( tsd2d(ji,jj) ) / MAX( ABS( 5.97_wp*ztransp ), 0.0000001_wp )
+ END_2D
+ DO_2D( 1, 0, 1, 0 ) ! exp. wave number & Stokes drift velocity at u- & v-points
+ zk_u(ji,jj) = 0.5_wp * ( zk_t(ji,jj) + zk_t(ji+1,jj) )
+ zk_v(ji,jj) = 0.5_wp * ( zk_t(ji,jj) + zk_t(ji,jj+1) )
+ !
+ zu0_sd(ji,jj) = 0.5_wp * ( ut0sd(ji,jj) + ut0sd(ji+1,jj) )
+ zv0_sd(ji,jj) = 0.5_wp * ( vt0sd(ji,jj) + vt0sd(ji,jj+1) )
+ END_2D
+
+ ! !== horizontal Stokes Drift 3D velocity ==!
+
+ DO_3D( 0, 0, 0, 0, 1, jpkm1 )
+ zdep_u = 0.5_wp * ( gdept(ji,jj,jk,Kmm) + gdept(ji+1,jj,jk,Kmm) )
+ zdep_v = 0.5_wp * ( gdept(ji,jj,jk,Kmm) + gdept(ji,jj+1,jk,Kmm) )
+ !
+ zkh_u = zk_u(ji,jj) * zdep_u ! k * depth
+ zkh_v = zk_v(ji,jj) * zdep_v
+ ! ! Depth attenuation
+ zda_u = EXP( -2.0_wp*zkh_u ) / ( 1.0_wp + 8.0_wp*zkh_u )
+ zda_v = EXP( -2.0_wp*zkh_v ) / ( 1.0_wp + 8.0_wp*zkh_v )
+ !
+ usd(ji,jj,jk) = zda_u * zu0_sd(ji,jj) * umask(ji,jj,jk)
+ vsd(ji,jj,jk) = zda_v * zv0_sd(ji,jj) * vmask(ji,jj,jk)
+ END_3D
+ ENDIF
+
+ CALL lbc_lnk( 'sbcwave', usd, 'U', -1.0_wp, vsd, 'V', -1.0_wp )
+
+ !
+ ! !== vertical Stokes Drift 3D velocity ==!
+ !
+ DO_3D( 0, 1, 0, 1, 1, jpkm1 ) ! Horizontal e3*divergence
+ ze3divh(ji,jj,jk) = ( e2u(ji ,jj) * e3u(ji ,jj,jk,Kmm) * usd(ji ,jj,jk) &
+ & - e2u(ji-1,jj) * e3u(ji-1,jj,jk,Kmm) * usd(ji-1,jj,jk) &
+ & + e1v(ji,jj ) * e3v(ji,jj ,jk,Kmm) * vsd(ji,jj ,jk) &
+ & - e1v(ji,jj-1) * e3v(ji,jj-1,jk,Kmm) * vsd(ji,jj-1,jk) ) &
+ & * r1_e1e2t(ji,jj)
+ END_3D
+ !
+ CALL lbc_lnk( 'sbcwave', ze3divh, 'T', 1.0_wp )
+ !
+ IF( ln_linssh ) THEN ; ik = 1 ! none zero velocity through the sea surface
+ ELSE ; ik = 2 ! w=0 at the surface (set one for all in sbc_wave_init)
+ ENDIF
+ DO jk = jpkm1, ik, -1 ! integrate from the bottom the hor. divergence (NB: at k=jpk w is always zero)
+ wsd(:,:,jk) = wsd(:,:,jk+1) - ze3divh(:,:,jk)
+ END DO
+ !
+ IF( ln_bdy ) THEN
+ DO jk = 1, jpkm1
+ wsd(:,:,jk) = wsd(:,:,jk) * bdytmask(:,:)
+ END DO
+ ENDIF
+ ! !== Horizontal divergence of barotropic Stokes transport ==!
+ div_sd(:,:) = 0._wp
+ DO jk = 1, jpkm1 !
+ div_sd(:,:) = div_sd(:,:) + ze3divh(:,:,jk)
+ END DO
+ !
+ CALL iom_put( "ustokes", usd )
+ CALL iom_put( "vstokes", vsd )
+ CALL iom_put( "wstokes", wsd )
+! !
+ DEALLOCATE( ze3divh, zInt_w )
+ DEALLOCATE( zk_t, zk_u, zk_v, zu0_sd, zv0_sd )
+ !
+ END SUBROUTINE sbc_stokes
+!
+!
+ SUBROUTINE sbc_wave( kt, Kmm )
+ !!---------------------------------------------------------------------
+ !! *** ROUTINE sbc_wave ***
+ !!
+ !! ** Purpose : read wave parameters from wave model in netcdf files
+ !! or from a coupled wave mdoel
+ !!
+ !!---------------------------------------------------------------------
+ INTEGER, INTENT(in ) :: kt ! ocean time step
+ INTEGER, INTENT(in ) :: Kmm ! ocean time index
+ !!---------------------------------------------------------------------
+ !
+ IF( kt == nit000 .AND. lwp ) THEN
+ WRITE(numout,*)
+ WRITE(numout,*) 'sbc_wave : update the read waves fields'
+ WRITE(numout,*) '~~~~~~~~ '
+ ENDIF
+ !
+ IF( ln_cdgw .AND. .NOT. cpl_wdrag ) THEN !== Neutral drag coefficient ==!
+ CALL fld_read( kt, nn_fsbc, sf_cd ) ! read from external forcing
+ cdn_wave(:,:) = sf_cd(1)%fnow(:,:,1) * tmask(:,:,1)
+ ENDIF
+
+ IF( ln_tauoc .AND. .NOT. cpl_wstrf ) THEN !== Wave induced stress ==!
+ CALL fld_read( kt, nn_fsbc, sf_tauoc ) ! read stress reduction factor due to wave from external forcing
+ tauoc_wave(:,:) = sf_tauoc(1)%fnow(:,:,1) * tmask(:,:,1)
+ ELSEIF ( ln_taw .AND. cpl_taw ) THEN
+ IF (kt < 1) THEN ! The first fields gave by OASIS have very high erroneous values ....
+ twox(:,:)=0._wp
+ twoy(:,:)=0._wp
+ tawx(:,:)=0._wp
+ tawy(:,:)=0._wp
+ tauoc_wavex(:,:) = 1._wp
+ tauoc_wavey(:,:) = 1._wp
+ ELSE
+ tauoc_wavex(:,:) = abs(twox(:,:)/tawx(:,:))
+ tauoc_wavey(:,:) = abs(twoy(:,:)/tawy(:,:))
+ ENDIF
+ ENDIF
+
+ ! Read also wave number if needed, so that it is available in coupling routines
+ IF( ln_STOKES_ADIAB .AND. .NOT. cpl_wnum ) THEN !==wavenumber==!
+ CALL fld_read( kt, nn_fsbc, sf_wn ) ! read wave parameters from external forcing
+ wnum(:,:) = sf_wn(1)%fnow(:,:,1) * tmask(:,:,1)
+ ENDIF
+
+ IF ( ln_phioc .and. cpl_phioc .and. kt == nit000 ) THEN
+ WRITE(numout,*)
+ WRITE(numout,*) 'sbc_wave : PHIOC from wave model'
+ WRITE(numout,*) '~~~~~~~~ '
+ ENDIF
+
+ IF( ln_sdw .AND. .NOT. cpl_sdrftx) THEN !== Computation of the 3d Stokes Drift ==!
+ !
+ IF( jpfld > 0 ) THEN ! Read from file only if the field is not coupled
+ CALL fld_read( kt, nn_fsbc, sf_sd ) ! read wave parameters from external forcing
+ ! ! NB: test case mode, not read as jpfld=0
+ IF( jp_hsw > 0 ) hsw (:,:) = sf_sd(jp_hsw)%fnow(:,:,1) * tmask(:,:,1) ! significant wave height
+ IF( jp_wmp > 0 ) wmp (:,:) = sf_sd(jp_wmp)%fnow(:,:,1) * tmask(:,:,1) ! wave mean period
+ IF( jp_usd > 0 ) ut0sd(:,:) = sf_sd(jp_usd)%fnow(:,:,1) * tmask(:,:,1) ! 2D zonal Stokes Drift at T point
+ IF( jp_vsd > 0 ) vt0sd(:,:) = sf_sd(jp_vsd)%fnow(:,:,1) * tmask(:,:,1) ! 2D meridional Stokes Drift at T point
+ ENDIF
+ !
+ IF( jpfld == 4 .OR. ln_wave_test ) &
+ & CALL sbc_stokes( Kmm ) ! Calculate only if all required fields are read
+ ! ! or in wave test case
+ ! ! ! In coupled case the call is done after (in sbc_cpl)
+ ENDIF
+ !
+ END SUBROUTINE sbc_wave
+
+
+ SUBROUTINE sbc_wave_init
+ !!---------------------------------------------------------------------
+ !! *** ROUTINE sbc_wave_init ***
+ !!
+ !! ** Purpose : Initialisation fo surface waves
+ !!
+ !! ** Method : - Read namelist namsbc_wave
+ !! - create the structure used to read required wave fields
+ !! (its size depends on namelist options)
+ !! ** action
+ !!---------------------------------------------------------------------
+ INTEGER :: ierror, ios ! local integer
+ INTEGER :: ifpr
+ !!
+ CHARACTER(len=100) :: cn_dir ! Root directory for location of drag coefficient files
+ TYPE(FLD_N), ALLOCATABLE, DIMENSION(:) :: slf_i ! array of namelist informations on the fields to read
+ TYPE(FLD_N) :: sn_cdg, sn_usd, sn_vsd, &
+ & sn_hsw, sn_wmp, sn_wnum, sn_tauoc ! informations about the fields to be read
+ !
+ NAMELIST/namsbc_wave/ cn_dir, sn_cdg, sn_usd, sn_vsd, sn_hsw, sn_wmp, sn_wnum, sn_tauoc, &
+ & ln_cdgw, ln_sdw, ln_tauoc, ln_stcor, ln_charn, ln_taw, ln_phioc, &
+ & ln_wave_test, ln_bern_srfc, ln_breivikFV_2016, ln_vortex_force, ln_stshear
+ !!---------------------------------------------------------------------
+ IF(lwp) THEN
+ WRITE(numout,*)
+ WRITE(numout,*) 'sbc_wave_init : surface waves in the system'
+ WRITE(numout,*) '~~~~~~~~~~~~~ '
+ ENDIF
+ !
+ READ ( numnam_ref, namsbc_wave, IOSTAT = ios, ERR = 901)
+901 IF( ios /= 0 ) CALL ctl_nam ( ios , 'namsbc_wave in reference namelist')
+
+ READ ( numnam_cfg, namsbc_wave, IOSTAT = ios, ERR = 902 )
+902 IF( ios > 0 ) CALL ctl_nam ( ios , 'namsbc_wave in configuration namelist' )
+ IF(lwm) WRITE ( numond, namsbc_wave )
+ !
+ IF(lwp) THEN
+ WRITE(numout,*) ' Namelist namsbc_wave'
+ WRITE(numout,*) ' Stokes drift ln_sdw = ', ln_sdw
+ WRITE(numout,*) ' Breivik 2016 ln_breivikFV_2016 = ', ln_breivikFV_2016
+ WRITE(numout,*) ' Stokes Coriolis & tracer advection terms ln_stcor = ', ln_stcor
+ WRITE(numout,*) ' Vortex Force ln_vortex_force = ', ln_vortex_force
+ WRITE(numout,*) ' Bernouilli Head Pressure ln_bern_srfc = ', ln_bern_srfc
+ WRITE(numout,*) ' wave modified ocean stress ln_tauoc = ', ln_tauoc
+ WRITE(numout,*) ' neutral drag coefficient (CORE bulk only) ln_cdgw = ', ln_cdgw
+ WRITE(numout,*) ' charnock coefficient ln_charn = ', ln_charn
+ WRITE(numout,*) ' Stress modificated by wave ln_taw = ', ln_taw
+ WRITE(numout,*) ' TKE flux from wave ln_phioc = ', ln_phioc
+ WRITE(numout,*) ' Surface shear with Stokes drift ln_stshear = ', ln_stshear
+ WRITE(numout,*) ' Test with constant wave fields ln_wave_test = ', ln_wave_test
+ ENDIF
+
+ ! ! option check
+ IF( .NOT.( ln_cdgw .OR. ln_sdw .OR. ln_tauoc .OR. ln_stcor .OR. ln_charn) ) &
+ & CALL ctl_warn( 'Ask for wave coupling but ln_cdgw=F, ln_sdw=F, ln_tauoc=F, ln_stcor=F')
+ IF( ln_cdgw .AND. ln_blk ) &
+ & CALL ctl_stop( 'drag coefficient read from wave model NOT available yet with aerobulk package')
+ IF( ln_stcor .AND. .NOT.ln_sdw ) &
+ & CALL ctl_stop( 'Stokes-Coriolis term calculated only if activated Stokes Drift ln_sdw=T')
+
+ ! !== Allocate wave arrays ==!
+ ALLOCATE( ut0sd (jpi,jpj) , vt0sd (jpi,jpj) )
+ ALLOCATE( hsw (jpi,jpj) , wmp (jpi,jpj) )
+ ALLOCATE( wnum (jpi,jpj) )
+ ALLOCATE( tsd2d (jpi,jpj) , div_sd(jpi,jpj) , bhd_wave(jpi,jpj) )
+ ALLOCATE( usd (jpi,jpj,jpk), vsd (jpi,jpj,jpk), wsd (jpi,jpj,jpk) )
+ ALLOCATE( tusd (jpi,jpj) , tvsd (jpi,jpj) , ZMX (jpi,jpj,jpk) )
+ usd (:,:,:) = 0._wp
+ vsd (:,:,:) = 0._wp
+ wsd (:,:,:) = 0._wp
+ hsw (:,:) = 0._wp
+ wmp (:,:) = 0._wp
+ ut0sd (:,:) = 0._wp
+ vt0sd (:,:) = 0._wp
+ tusd (:,:) = 0._wp
+ tvsd (:,:) = 0._wp
+ bhd_wave(:,:) = 0._wp
+ ZMX (:,:,:) = 0._wp
+!
+ IF( ln_wave_test ) THEN !== Wave TEST case ==! set uniform waves fields
+ jpfld = 0 ! No field read
+ ln_cdgw = .FALSE. ! No neutral wave drag input
+ ln_tauoc = .FALSE. ! No wave induced drag reduction factor
+ ut0sd(:,:) = 0.13_wp * tmask(:,:,1) ! m/s
+ vt0sd(:,:) = 0.00_wp ! m/s
+ hsw (:,:) = 2.80_wp ! meters
+ wmp (:,:) = 8.00_wp ! seconds
+ !
+ ELSE !== create the structure associated with fields to be read ==!
+ IF( ln_cdgw ) THEN ! wave drag
+ IF( .NOT. cpl_wdrag ) THEN
+ ALLOCATE( sf_cd(1), STAT=ierror ) !* allocate and fill sf_wave with sn_cdg
+ IF( ierror > 0 ) CALL ctl_stop( 'STOP', 'sbc_wave_init: unable to allocate sf_wave structure' )
+ !
+ ALLOCATE( sf_cd(1)%fnow(jpi,jpj,1) )
+ IF( sn_cdg%ln_tint ) ALLOCATE( sf_cd(1)%fdta(jpi,jpj,1,2) )
+ CALL fld_fill( sf_cd, (/ sn_cdg /), cn_dir, 'sbc_wave_init', 'Wave module ', 'namsbc_wave' )
+ ENDIF
+ ALLOCATE( cdn_wave(jpi,jpj) )
+ cdn_wave(:,:) = 0._wp
+ ENDIF
+ IF( ln_charn ) THEN ! wave drag
+ IF( .NOT. cpl_charn ) THEN
+ CALL ctl_stop( 'STOP', 'Charnock based wind stress can be used in coupled mode only' )
+ ENDIF
+ ALLOCATE( charn(jpi,jpj) )
+ charn(:,:) = 0._wp
+ ENDIF
+ IF( ln_taw ) THEN ! wind stress
+ IF( .NOT. cpl_taw ) THEN
+ CALL ctl_stop( 'STOP', 'wind stress from wave model can be used in coupled mode only, use ln_cdgw instead' )
+ ENDIF
+ ALLOCATE( tawx(jpi,jpj) )
+ ALLOCATE( tawy(jpi,jpj) )
+ ALLOCATE( twox(jpi,jpj) )
+ ALLOCATE( twoy(jpi,jpj) )
+ ALLOCATE( tauoc_wavex(jpi,jpj) )
+ ALLOCATE( tauoc_wavey(jpi,jpj) )
+ tawx(:,:) = 0._wp
+ tawy(:,:) = 0._wp
+ twox(:,:) = 0._wp
+ twoy(:,:) = 0._wp
+ tauoc_wavex(:,:) = 1._wp
+ tauoc_wavey(:,:) = 1._wp
+ ENDIF
+
+ IF( ln_phioc ) THEN ! TKE flux
+ IF( .NOT. cpl_phioc ) THEN
+ CALL ctl_stop( 'STOP', 'phioc can be used in coupled mode only' )
+ ENDIF
+ ALLOCATE( phioc(jpi,jpj) )
+ phioc(:,:) = 0._wp
+ ENDIF
+
+ IF( ln_tauoc ) THEN ! normalized wave stress into the ocean
+ IF( .NOT. cpl_wstrf ) THEN
+ ALLOCATE( sf_tauoc(1), STAT=ierror ) !* allocate and fill sf_wave with sn_tauoc
+ IF( ierror > 0 ) CALL ctl_stop( 'STOP', 'sbc_wave_init: unable to allocate sf_tauoc structure' )
+ !
+ ALLOCATE( sf_tauoc(1)%fnow(jpi,jpj,1) )
+ IF( sn_tauoc%ln_tint ) ALLOCATE( sf_tauoc(1)%fdta(jpi,jpj,1,2) )
+ CALL fld_fill( sf_tauoc, (/ sn_tauoc /), cn_dir, 'sbc_wave_init', 'Wave module', 'namsbc_wave' )
+ ENDIF
+ ALLOCATE( tauoc_wave(jpi,jpj) )
+ tauoc_wave(:,:) = 0._wp
+ ENDIF
+
+ IF( ln_sdw ) THEN ! Stokes drift
+ ! 1. Find out how many fields have to be read from file if not coupled
+ jpfld=0
+ jp_usd=0 ; jp_vsd=0 ; jp_hsw=0 ; jp_wmp=0
+ IF( .NOT. cpl_sdrftx ) THEN
+ jpfld = jpfld + 1
+ jp_usd = jpfld
+ ENDIF
+ IF( .NOT. cpl_sdrfty ) THEN
+ jpfld = jpfld + 1
+ jp_vsd = jpfld
+ ENDIF
+ IF( .NOT. cpl_hsig ) THEN
+ jpfld = jpfld + 1
+ jp_hsw = jpfld
+ ENDIF
+ IF( .NOT. cpl_wper ) THEN
+ jpfld = jpfld + 1
+ jp_wmp = jpfld
+ ENDIF
+ ! 2. Read from file only the non-coupled fields
+ IF( jpfld > 0 ) THEN
+ ALLOCATE( slf_i(jpfld) )
+ IF( jp_usd > 0 ) slf_i(jp_usd) = sn_usd
+ IF( jp_vsd > 0 ) slf_i(jp_vsd) = sn_vsd
+ IF( jp_hsw > 0 ) slf_i(jp_hsw) = sn_hsw
+ IF( jp_wmp > 0 ) slf_i(jp_wmp) = sn_wmp
+ ALLOCATE( sf_sd(jpfld), STAT=ierror ) !* allocate and fill sf_sd with stokes drift
+ IF( ierror > 0 ) CALL ctl_stop( 'STOP', 'sbc_wave_init: unable to allocate sf_wave structure' )
+ !
+ DO ifpr= 1, jpfld
+ ALLOCATE( sf_sd(ifpr)%fnow(jpi,jpj,1) )
+ IF( slf_i(ifpr)%ln_tint ) ALLOCATE( sf_sd(ifpr)%fdta(jpi,jpj,1,2) )
+ END DO
+ !
+ CALL fld_fill( sf_sd, slf_i, cn_dir, 'sbc_wave_init', 'Wave module ', 'namsbc_wave' )
+ sf_sd(jp_usd)%zsgn = -1._wp ; sf_sd(jp_vsd)%zsgn = -1._wp ! vector field at T point: overwrite default definition of zsgn
+ ENDIF
+ !
+ ! 3. Wave number (only needed for Qiao parametrisation,
+ ! ln_zdfqiao=T)
+ IF( ln_STOKES_ADIAB ) THEN
+ IF( .NOT. cpl_wnum ) THEN
+ ALLOCATE( sf_wn(1), STAT=ierror ) !* allocate and fill sf_wave with sn_wnum
+ IF( ierror > 0 ) CALL ctl_stop( 'STOP', 'sbc_wave_init: unable to allocate sf_wn structure' )
+ ALLOCATE( sf_wn(1)%fnow(jpi,jpj,1) )
+ IF( sn_wnum%ln_tint ) ALLOCATE( sf_wn(1)%fdta(jpi,jpj,1,2) )
+ CALL fld_fill( sf_wn, (/ sn_wnum /), cn_dir, 'sbc_wave_init', 'Wave module', 'namsbc_wave' )
+ ENDIF
+ ENDIF
+ !
+ !
+ ENDIF
+ !
+ ENDIF
+ !
+ END SUBROUTINE sbc_wave_init
+
+ !!======================================================================
+END MODULE sbcwave
diff --git a/tests/ADIAB_WAVE/MY_SRC/usrdef_hgr.F90 b/tests/ADIAB_WAVE/MY_SRC/usrdef_hgr.F90
new file mode 100644
index 000000000..12f778f5c
--- /dev/null
+++ b/tests/ADIAB_WAVE/MY_SRC/usrdef_hgr.F90
@@ -0,0 +1,115 @@
+MODULE usrdef_hgr
+ !!======================================================================
+ !! *** MODULE usrdef_hgr ***
+ !!
+ !! === ADIAB_WAVE configuration ===
+ !!
+ !! User defined : mesh and Coriolis parameter of a user configuration
+ !!======================================================================
+ !! History : 4.0 ! 2016-03 (S. Flavoni)
+ !!----------------------------------------------------------------------
+
+ !!----------------------------------------------------------------------
+ !! usr_def_hgr : initialize the horizontal mesh
+ !!----------------------------------------------------------------------
+ USE dom_oce ! ocean space and time domain
+ USE par_oce ! ocean space and time domain
+ USE phycst ! physical constants
+ USE usrdef_nam !
+ !
+ USE in_out_manager ! I/O manager
+ USE lib_mpp ! MPP library
+
+ IMPLICIT NONE
+ PRIVATE
+
+ PUBLIC usr_def_hgr ! called in domhgr.F90
+
+ !! * Substitutions
+# include "do_loop_substitute.h90"
+ !!----------------------------------------------------------------------
+ !! NEMO/OCE 4.0 , NEMO Consortium (2018)
+ !! $Id: usrdef_hgr.F90 14223 2020-12-19 10:22:45Z smasson $
+ !! Software governed by the CeCILL license (see ./LICENSE)
+ !!----------------------------------------------------------------------
+CONTAINS
+
+ SUBROUTINE usr_def_hgr( plamt , plamu , plamv , plamf , & ! geographic position (required)
+ & pphit , pphiu , pphiv , pphif , & !
+ & kff , pff_f , pff_t , & ! Coriolis parameter (if domain not on the sphere)
+ & pe1t , pe1u , pe1v , pe1f , & ! scale factors (required)
+ & pe2t , pe2u , pe2v , pe2f , & !
+ & ke1e2u_v , pe1e2u , pe1e2v ) ! u- & v-surfaces (if gridsize reduction is used in strait(s))
+ !!----------------------------------------------------------------------
+ !! *** ROUTINE usr_def_hgr ***
+ !!
+ !! ** Purpose : user defined mesh and Coriolis parameter
+ !!
+ !! ** Method : set all intent(out) argument to a proper value
+ !!
+ !! Here ADIAB_WAVE configuration :
+ !! Rectangular domain
+ !! - a constant horizontal resolution of 10m
+ !! - no Coriolis effect
+ !!
+ !! ** Action : - define longitude & latitude of t-, u-, v- and f-points (in degrees)
+ !! - define coriolis parameter at f-point if the domain in not on the sphere (on beta-plane)
+ !! - define i- & j-scale factors at t-, u-, v- and f-points (in meters)
+ !! - define u- & v-surfaces (if gridsize reduction is used in some straits) (in m2)
+ !!----------------------------------------------------------------------
+ REAL(wp), DIMENSION(:,:), INTENT(out) :: plamt, plamu, plamv, plamf ! longitude outputs [degrees]
+ REAL(wp), DIMENSION(:,:), INTENT(out) :: pphit, pphiu, pphiv, pphif ! latitude outputs [degrees]
+ INTEGER , INTENT(out) :: kff ! =1 Coriolis parameter computed here, =0 otherwise
+ REAL(wp), DIMENSION(:,:), INTENT(out) :: pff_f, pff_t ! Coriolis factor at f-point [1/s]
+ REAL(wp), DIMENSION(:,:), INTENT(out) :: pe1t, pe1u, pe1v, pe1f ! i-scale factors [m]
+ REAL(wp), DIMENSION(:,:), INTENT(out) :: pe2t, pe2u, pe2v, pe2f ! j-scale factors [m]
+ INTEGER , INTENT(out) :: ke1e2u_v ! =1 u- & v-surfaces computed here, =0 otherwise
+ REAL(wp), DIMENSION(:,:), INTENT(out) :: pe1e2u, pe1e2v ! u- & v-surfaces (if reduction in strait) [m2]
+ !
+ INTEGER :: ji, jj ! dummy loop indices
+ REAL(wp) :: zfact, zfact2 ! local scalars
+ !!-------------------------------------------------------------------------------
+ !
+ !
+ IF(lwp) WRITE(numout,*) 'usr_def_hgr : ADIAB_GLM configuration bassin'
+ IF(lwp) WRITE(numout,*) '~~~~~~~~~~~ uniform grid spacing WITHOUT Coriolis force (f=0)'
+ !
+ ! !== grid point position ==! (in kilometers)
+ zfact = rn_dx * 1.e-3 ! conversion in km
+ zfact2= rn_dy * 1.e-3
+ DO_2D( nn_hls, nn_hls, nn_hls, nn_hls )
+! DO_2D( 1, 1, 1, 1 )
+ ! ! longitude
+ plamt(ji,jj) = zfact * ( REAL( mig0(ji)-1 , wp ) )
+ plamu(ji,jj) = zfact * ( 0.5 + REAL( mig0(ji)-1 , wp ) )
+ plamv(ji,jj) = plamt(ji,jj)
+ plamf(ji,jj) = plamu(ji,jj)
+ ! ! latitude
+ pphit(ji,jj) = zfact2 * ( REAL( mjg0(jj)-1 , wp ) )
+ pphiu(ji,jj) = pphit(ji,jj)
+ pphiv(ji,jj) = zfact2 * ( 0.5 + REAL( mjg0(jj)-1 , wp ) )
+ pphif(ji,jj) = pphiv(ji,jj)
+ END_2D
+ !
+ ! !== Horizontal scale factors ==! (in meters)
+ pe1t(:,:) = rn_dx ; pe2t(:,:) = rn_dy
+ pe1u(:,:) = rn_dx ; pe2u(:,:) = rn_dy
+ pe1v(:,:) = rn_dx ; pe2v(:,:) = rn_dy
+ pe1f(:,:) = rn_dx ; pe2f(:,:) = rn_dy
+ !
+ ! ! NO reduction of grid size in some straits
+ ke1e2u_v = 0 ! ==>> u_ & v_surfaces will be computed in dom_ghr routine
+ pe1e2u(:,:) = 0._wp ! CAUTION: set to zero to avoid error with some compilers that
+ pe1e2v(:,:) = 0._wp ! require an initialization of INTENT(out) arguments
+ !
+ !
+ ! !== Coriolis parameter ==!
+ kff = 1 ! indicate not to compute Coriolis parameter afterward
+ !
+ pff_f(:,:) = 0._wp ! here No earth rotation: f=0
+ pff_t(:,:) = 0._wp
+ !
+ END SUBROUTINE usr_def_hgr
+
+ !!======================================================================
+END MODULE usrdef_hgr
diff --git a/tests/ADIAB_WAVE/MY_SRC/usrdef_istate.F90 b/tests/ADIAB_WAVE/MY_SRC/usrdef_istate.F90
new file mode 100644
index 000000000..ff659b0fb
--- /dev/null
+++ b/tests/ADIAB_WAVE/MY_SRC/usrdef_istate.F90
@@ -0,0 +1,91 @@
+MODULE usrdef_istate
+ !!======================================================================
+ !! *** MODULE usrdef_istate ***
+ !!
+ !! === ADIAB_WAVE configuration ===
+ !!
+ !! User defined : set the initial state of a user configuration
+ !!======================================================================
+ !! History : 4.0 ! 2016-03 (S. Flavoni) Original code
+ !! ! 2020-11 (S. Techene, G. Madec) separate tsuv from ssh
+ !!----------------------------------------------------------------------
+
+ !!----------------------------------------------------------------------
+ !! usr_def_istate : initial state in Temperature and salinity
+ !!----------------------------------------------------------------------
+ USE par_oce ! ocean space and time domain
+ USE phycst ! physical constants
+ !
+ USE in_out_manager ! I/O manager
+ USE lib_mpp ! MPP library
+
+ IMPLICIT NONE
+ PRIVATE
+
+ PUBLIC usr_def_istate ! called in istate.F90
+ PUBLIC usr_def_istate_ssh ! called by domqco.F90
+
+ !! * Substitutions
+# include "do_loop_substitute.h90"
+ !!----------------------------------------------------------------------
+ !! NEMO/OCE 4.0 , NEMO Consortium (2018)
+ !! $Id: usrdef_istate.F90 14834 2021-05-11 09:24:44Z hadcv $
+ !! Software governed by the CeCILL license (see ./LICENSE)
+ !!----------------------------------------------------------------------
+CONTAINS
+
+ SUBROUTINE usr_def_istate( pdept, ptmask, pts, pu, pv )
+ !!----------------------------------------------------------------------
+ !! *** ROUTINE usr_def_istate ***
+ !!
+ !! ** Purpose : Initialization of the dynamics and tracers
+ !! Here ADIAB_WAVE configuration
+ !!
+ !! ** Method : - set temprature field
+ !! - set salinity field
+ !!----------------------------------------------------------------------
+ REAL(wp), DIMENSION(jpi,jpj,jpk) , INTENT(in ) :: pdept ! depth of t-point [m]
+ REAL(wp), DIMENSION(jpi,jpj,jpk) , INTENT(in ) :: ptmask ! t-point ocean mask [m]
+ REAL(wp), DIMENSION(jpi,jpj,jpk,jpts), INTENT( out) :: pts ! T & S fields [Celsius ; g/kg]
+ REAL(wp), DIMENSION(jpi,jpj,jpk) , INTENT( out) :: pu ! i-component of the velocity [m/s]
+ REAL(wp), DIMENSION(jpi,jpj,jpk) , INTENT( out) :: pv ! j-component of the velocity [m/s]
+ !
+ INTEGER :: ji, jj, jk ! dummy loop indices
+ !!----------------------------------------------------------------------
+ !
+ IF(lwp) WRITE(numout,*)
+ IF(lwp) WRITE(numout,*) 'usr_def_istate : analytical definition of initial state '
+ IF(lwp) WRITE(numout,*) '~~~~~~~~~~~~~~ Ocean at rest, with a constant T and S'
+ !
+ pu (:,:,:) = 0._wp ! ocean at rest
+ pv (:,:,:) = 0._wp
+ !
+ pts(:,:,:,jp_tem) = 25._wp
+ pts(:,:,:,jp_sal) = 35._wp
+ !
+ END SUBROUTINE usr_def_istate
+
+
+ SUBROUTINE usr_def_istate_ssh( ptmask, pssh )
+ !!----------------------------------------------------------------------
+ !! *** ROUTINE usr_def_istate_ssh ***
+ !!
+ !! ** Purpose : Initialization of ssh
+ !!
+ !! ** Method : Set ssh as null, ptmask is required for test cases
+ !!----------------------------------------------------------------------
+ REAL(wp), DIMENSION(jpi,jpj,jpk) , INTENT(in ) :: ptmask ! t-point ocean mask [m]
+ REAL(wp), DIMENSION(jpi,jpj) , INTENT( out) :: pssh ! sea-surface height [m]
+ !!----------------------------------------------------------------------
+ !
+ IF(lwp) WRITE(numout,*)
+ IF(lwp) WRITE(numout,*) 'usr_def_istate_ssh : analytical definition of initial state'
+ IF(lwp) WRITE(numout,*) '~~~~~~~~~~~~~~~~~~ Ocean at rest, ssh is zero'
+ !
+ ! Sea level:
+ pssh(:,:) = 0._wp
+ !
+ END SUBROUTINE usr_def_istate_ssh
+
+ !!======================================================================
+END MODULE usrdef_istate
diff --git a/tests/ADIAB_WAVE/MY_SRC/usrdef_nam.F90 b/tests/ADIAB_WAVE/MY_SRC/usrdef_nam.F90
new file mode 100644
index 000000000..82ae39ac7
--- /dev/null
+++ b/tests/ADIAB_WAVE/MY_SRC/usrdef_nam.F90
@@ -0,0 +1,112 @@
+MODULE usrdef_nam
+ !!======================================================================
+ !! *** MODULE usrdef_nam ***
+ !!
+ !! === ADIAB_WAVE configuration ===
+ !!
+ !! User defined : set the domain characteristics of a user configuration
+ !!======================================================================
+ !! History : 4.0 ! 2016-03 (S. Flavoni, G. Madec) Original code
+ !!----------------------------------------------------------------------
+
+ !!----------------------------------------------------------------------
+ !! usr_def_nam : read user defined namelist and set global domain size
+ !! usr_def_hgr : initialize the horizontal mesh
+ !!----------------------------------------------------------------------
+ USE dom_oce
+ USE par_oce ! ocean space and time domain
+ USE phycst ! physical constants
+ !
+ USE in_out_manager ! I/O manager
+ USE lib_mpp ! MPP library
+
+ IMPLICIT NONE
+ PRIVATE
+
+ PUBLIC usr_def_nam ! called in nemogcm.F90 module
+
+ ! !!* namusr_def namelist *!!
+ REAL(wp), PUBLIC :: rn_dx ! resolution in meters defining the horizontal domain size
+ REAL(wp), PUBLIC :: rn_dz ! resolution in meters defining the vertical domain size
+ REAL(wp), PUBLIC :: rn_dy
+ LOGICAL , PUBLIC :: ln_STOKES_ADIAB !Shallow/Inter water formula for the Stokes drift
+ !!----------------------------------------------------------------------
+ !! NEMO/OCE 4.0 , NEMO Consortium (2018)
+ !! $Id: usrdef_nam.F90 14433 2021-02-11 08:06:49Z smasson $
+ !! Software governed by the CeCILL license (see ./LICENSE)
+ !!----------------------------------------------------------------------
+CONTAINS
+
+ SUBROUTINE usr_def_nam( cd_cfg, kk_cfg, kpi, kpj, kpk, ldIperio, ldJperio, ldNFold, cdNFtype )
+ !!----------------------------------------------------------------------
+ !! *** ROUTINE dom_nam ***
+ !!
+ !! ** Purpose : read user defined namelist and define the domain size
+ !!
+ !! ** Method : read in namusr_def containing all the user specific namelist parameter
+ !!
+ !! Here ADIAB configuration
+ !!
+ !! ** input : - namusr_def namelist found in namelist_cfg
+ !!----------------------------------------------------------------------
+ CHARACTER(len=*), INTENT(out) :: cd_cfg ! configuration name
+ INTEGER , INTENT(out) :: kk_cfg ! configuration resolution
+ INTEGER , INTENT(out) :: kpi, kpj, kpk ! global domain sizes
+ LOGICAL , INTENT(out) :: ldIperio, ldJperio ! i- and j- periodicity
+ LOGICAL , INTENT(out) :: ldNFold ! North pole folding
+ CHARACTER(len=1), INTENT(out) :: cdNFtype ! Folding type: T or F
+ !
+ INTEGER :: ios ! Local integer
+ !!
+ NAMELIST/namusr_def/ ln_zco, ln_zps, ln_sco, rn_dx, rn_dy, rn_dz, ln_STOKES_ADIAB
+ !!----------------------------------------------------------------------
+ !
+ READ ( numnam_cfg, namusr_def, IOSTAT = ios, ERR = 902 )
+902 IF( ios /= 0 ) CALL ctl_nam ( ios , 'namusr_def in configuration namelist' )
+ !
+ IF(lwm) WRITE( numond, namusr_def )
+ !
+ cd_cfg = 'ADIAB_WAVE' ! name & resolution
+ kk_cfg = INT( rn_dx )
+ !
+ ! Global Domain size: ADIAB_WAVE domain is 780 km x 5 grid-points x 4-6 m
+ kpi = INT( 780. / rn_dx )
+ kpj = 5. !
+ kpk = INT( 6. / rn_dz )!+1
+
+ ! ! Set the lateral boundary condition of the global domain
+ ldIperio = .FALSE. ; ldJperio = .FALSE. ! closed domain
+ ldNFold = .FALSE. ; cdNFtype = '-'
+ !
+ ! ! control print
+ IF(lwp) THEN
+ WRITE(numout,*) ' '
+ WRITE(numout,*) 'usr_def_nam : read the user defined namelist (namusr_def) in namelist_cfg'
+ WRITE(numout,*) '~~~~~~~~~~~ '
+ WRITE(numout,*) ' Namelist namusr_def : ADIAB_WAVE test case'
+ WRITE(numout,*) ' type of vertical coordinate : '
+ WRITE(numout,*) ' z-coordinate flag ln_zco = ', ln_zco
+ WRITE(numout,*) ' z-partial-step coordinate flag ln_zps = ', ln_zps
+ WRITE(numout,*) ' s-coordinate flag ln_sco = ', ln_sco
+ WRITE(numout,*) ' horizontal resolution rn_dx = ', rn_dx, ' meters'
+ WRITE(numout,*) ' horizontal resolution rn_dy = ', rn_dy, ' meters'
+ WRITE(numout,*) ' vertical resolution rn_dz = ', rn_dz, ' meters'
+ WRITE(numout,*) ' ADIAB_WAVE domain = 780 m x 5 grid-points x 4-6 m'
+ WRITE(numout,*) ' resulting global domain size : Ni0glo = ', kpi
+ WRITE(numout,*) ' Nj0glo = ', kpj
+ WRITE(numout,*) ' jpkglo = ', kpk
+ WRITE(numout,*) ' Stokes drift for Shallow/Inter water (ln_STOKES_ADIAB) = ', ln_STOKES_ADIAB
+ !
+ ! Set the lateral boundary condition of the
+ ! ADIAB_WAVE configuration : E-W periodic basin
+ !
+ WRITE(numout,*) ' '
+ WRITE(numout,*) ' Lateral boundary condition of the global domain'
+ WRITE(numout,*) ' ADIAB_WAVE : closed basin Iperio =', ldIperio
+ WRITE(numout,*) ' ADIAB_WAVE : closed basin Jperio =', ldJperio
+ ENDIF
+ !
+ END SUBROUTINE usr_def_nam
+
+ !!======================================================================
+END MODULE usrdef_nam
diff --git a/tests/ADIAB_WAVE/MY_SRC/usrdef_sbc.F90 b/tests/ADIAB_WAVE/MY_SRC/usrdef_sbc.F90
new file mode 100644
index 000000000..8381c2e83
--- /dev/null
+++ b/tests/ADIAB_WAVE/MY_SRC/usrdef_sbc.F90
@@ -0,0 +1,95 @@
+MODULE usrdef_sbc
+ !!======================================================================
+ !! *** MODULE usrdef_sbc ***
+ !!
+ !! === ADIAB_WAVE configuration ===
+ !!
+ !! User defined : surface forcing of a user configuration
+ !!======================================================================
+ !! History : 4.0 ! 2016-03 (S. Flavoni, G. Madec) user defined interface
+ !!----------------------------------------------------------------------
+
+ !!----------------------------------------------------------------------
+ !! usrdef_sbc : user defined surface bounday conditions in ADIAB_WAVE case
+ !!----------------------------------------------------------------------
+ USE oce ! ocean dynamics and tracers
+ USE dom_oce ! ocean space and time domain
+ USE sbc_oce ! Surface boundary condition: ocean fields
+ USE sbcwave
+ USE phycst ! physical constants
+ !
+ USE in_out_manager ! I/O manager
+ USE lib_mpp ! distribued memory computing library
+ USE lbclnk ! ocean lateral boundary conditions (or mpp link)
+ USE lib_fortran !
+
+ IMPLICIT NONE
+ PRIVATE
+
+ PUBLIC usrdef_sbc_oce ! routine called in sbcmod module
+ PUBLIC usrdef_sbc_ice_tau ! routine called by icestp.F90 for ice dynamics
+ PUBLIC usrdef_sbc_ice_flx ! routine called by icestp.F90 for ice thermo
+
+ !! * Substitutions
+# include "do_loop_substitute.h90"
+ !!----------------------------------------------------------------------
+ !! NEMO/OCE 4.0 , NEMO Consortium (2018)
+ !! $Id: usrdef_sbc.F90 14433 2021-02-11 08:06:49Z smasson $
+ !! Software governed by the CeCILL license (see ./LICENSE)
+ !!----------------------------------------------------------------------
+CONTAINS
+
+ SUBROUTINE usrdef_sbc_oce( kt, Kbb )
+ !!---------------------------------------------------------------------
+ !! *** ROUTINE usrdef_sbc ***
+ !!
+ !! ** Purpose : provide at each time-step the ADIAB_WAVE surface boundary
+ !! condition, i.e. the momentum, heat and freshwater fluxes.
+ !!
+ !! ** Action : - set the ocean surface boundary condition, i.e.
+ !! utau, vtau, taum, wndm, qns, qsr, emp, sfx
+ !!
+ !! Reference : Hazeleger, W., and S. Drijfhout, JPO, 30, 677-695, 2000.
+ !!----------------------------------------------------------------------
+ INTEGER, INTENT(in) :: kt ! ocean time step
+ INTEGER, INTENT(in) :: Kbb ! ocean time index
+ !!
+ !!---------------------------------------------------------------------
+ ! ---------------------------------- !
+ ! control print at first time-step !
+ ! ---------------------------------- !
+ IF( kt == nit000 .AND. lwp ) THEN
+ WRITE(numout,*)
+ WRITE(numout,*)'usrdef_sbc_oce : ADIAB_WAVE case: NO surface forcing'
+ WRITE(numout,*)'utau = vtau = taum = wndm = qns = qsr = emp = sfx = 0'
+ WRITE(numout,*)'~~~~~~~~~~~ '
+ ENDIF
+ !
+ utau(:,:) = 0._wp
+ vtau(:,:) = 0._wp
+ taum(:,:) = 0._wp
+ wndm(:,:) = 0._wp
+ !
+ emp (:,:) = 0._wp
+ sfx (:,:) = 0._wp
+ qns (:,:) = 0._wp
+ qsr (:,:) = 0._wp
+
+ !
+ IF( ln_wave ) CALL sbc_wave ( kt, Kbb )
+ END SUBROUTINE usrdef_sbc_oce
+
+
+ SUBROUTINE usrdef_sbc_ice_tau( kt )
+ INTEGER, INTENT(in) :: kt ! ocean time step
+ END SUBROUTINE usrdef_sbc_ice_tau
+
+
+ SUBROUTINE usrdef_sbc_ice_flx( kt, phs, phi )
+ INTEGER, INTENT(in) :: kt ! ocean time step
+ REAL(wp), DIMENSION(:,:,:), INTENT(in) :: phs ! snow thickness
+ REAL(wp), DIMENSION(:,:,:), INTENT(in) :: phi ! ice thickness
+ END SUBROUTINE usrdef_sbc_ice_flx
+
+ !!======================================================================
+END MODULE usrdef_sbc
diff --git a/tests/ADIAB_WAVE/MY_SRC/usrdef_zgr.F90 b/tests/ADIAB_WAVE/MY_SRC/usrdef_zgr.F90
new file mode 100644
index 000000000..5c7fac838
--- /dev/null
+++ b/tests/ADIAB_WAVE/MY_SRC/usrdef_zgr.F90
@@ -0,0 +1,275 @@
+MODULE usrdef_zgr
+ !!======================================================================
+ !! *** MODULE usrdef_zgr ***
+ !!
+ !! === ADIAB_WAVE case ===
+ !!
+ !! user defined : vertical coordinate system of a user configuration
+ !!======================================================================
+ !! History : 4.0 ! 2016-08 (G. Madec, S. Flavoni) Original code
+ !!----------------------------------------------------------------------
+
+ !!----------------------------------------------------------------------
+ !! usr_def_zgr : user defined vertical coordinate system (required)
+ !! zgr_z1d : reference 1D z-coordinate
+ !!---------------------------------------------------------------------
+ USE oce ! ocean variables
+ USE dom_oce , ONLY: mi0, mi1 ! ocean space and time domain
+ USE dom_oce , ONLY: glamt ! ocean space and time domain
+ USE usrdef_nam ! User defined : namelist variables
+ !
+ USE in_out_manager ! I/O manager
+ USE lbclnk ! ocean lateral boundary conditions (or mpp link)
+ USE lib_mpp ! distributed memory computing library
+ USE timing ! Timing
+
+ IMPLICIT NONE
+ PRIVATE
+
+ PUBLIC usr_def_zgr ! called by domzgr.F90
+
+ !! * Substitutions
+# include "do_loop_substitute.h90"
+ !!----------------------------------------------------------------------
+ !! NEMO/OCE 4.0 , NEMO Consortium (2018)
+ !! $Id: usrdef_zgr.F90 14857 2021-05-12 16:47:25Z hadcv $
+ !! Software governed by the CeCILL license (see ./LICENSE)
+ !!----------------------------------------------------------------------
+CONTAINS
+
+ SUBROUTINE usr_def_zgr( ld_zco , ld_zps , ld_sco , ld_isfcav, & ! type of vertical coordinate
+ & pdept_1d, pdepw_1d, pe3t_1d , pe3w_1d , & ! 1D reference vertical coordinate
+ & pdept , pdepw , & ! 3D t & w-points depth
+ & pe3t , pe3u , pe3v , pe3f , & ! vertical scale factors
+ & pe3w , pe3uw , pe3vw, & ! - - -
+ & k_top , k_bot ) ! top & bottom ocean level
+ !!---------------------------------------------------------------------
+ !! *** ROUTINE usr_def_zgr ***
+ !!
+ !! ** Purpose : User defined the vertical coordinates
+ !!
+ !!----------------------------------------------------------------------
+ LOGICAL , INTENT(in ) :: ld_zco, ld_zps, ld_sco ! vertical coordinate flags ( read in namusr_def )
+ LOGICAL , INTENT( out) :: ld_isfcav ! under iceshelf cavity flag
+ REAL(wp), DIMENSION(:) , INTENT( out) :: pdept_1d, pdepw_1d ! 1D grid-point depth [m]
+ REAL(wp), DIMENSION(:) , INTENT( out) :: pe3t_1d , pe3w_1d ! 1D grid-point depth [m]
+ REAL(wp), DIMENSION(:,:,:), INTENT( out) :: pdept, pdepw ! grid-point depth [m]
+ REAL(wp), DIMENSION(:,:,:), INTENT( out) :: pe3t , pe3u , pe3v , pe3f ! vertical scale factors [m]
+ REAL(wp), DIMENSION(:,:,:), INTENT( out) :: pe3w , pe3uw, pe3vw ! i-scale factors
+ INTEGER , DIMENSION(:,:) , INTENT( out) :: k_top, k_bot ! first & last ocean level
+ !
+ INTEGER :: ji, jj, jk ! dummy indices
+ INTEGER :: ik, jii ! local integers
+ REAL(wp) :: zfact, z1_jpkm1 ! local scalar
+ REAL(wp) :: ze3min ! local scalar
+ REAL(wp), DIMENSION(jpi,jpj) :: zht, zhu, z2d ! 2D workspace
+ REAL(wp) :: a0=0.261799388 !local scalar def bathy
+ REAL(wp) :: h1=6.0 !local scalar def bathy
+ REAL(wp) :: h2=4.0 !local scalar def bathy
+ REAL(wp) :: x2
+ COMPLEX , PARAMETER :: i=(0,1) !sqrt(-1)
+ !!----------------------------------------------------------------------
+ !
+ IF(lwp) WRITE(numout,*)
+ IF(lwp) WRITE(numout,*) 'usr_def_zgr : ADIAB_WAVE configuration (s-coordinate closed box ocean without cavities)'
+ IF(lwp) WRITE(numout,*) '~~~~~~~~~~~'
+ !
+ !
+ ! type of vertical coordinate
+ ! ---------------------------
+ ! already set in usrdef_nam.F90 by reading the namusr_def namelist except for ISF
+ ld_isfcav = .FALSE.
+ !
+ zht(:,:)=h1
+ ! ! ====================
+ ! ! ADIAB_WAVE configuration
+ ! ! ====================
+ IF(lwp) WRITE(numout,*)
+ IF(lwp) WRITE(numout,*) 'usr_def_zgr (ADIAB_WAVE) : Ardhuin et al 2007 formulation'
+ IF(lwp) WRITE(numout,*) '~~~~~~~~~~'
+ !
+ DO ji = 1,40
+ x2=(-200+(ji-1)*10)
+ x2=(x2*a0-h2+h1)/h2
+ zht(ji,:)=-AIMAG((h1*(x2-i*a0)+(h2-h1)*LOG(1+EXP(x2-i*a0)))/a0)
+ END DO
+ DO jii=1, 40
+ zht(jpi*0.5+jii,:)=zht(jpi*0.5+1-jii,:)
+ ENDDO
+
+ IF(lwp) WRITE(numout,*) 'zht', zht(:,1)
+ !
+ ! at u-point: averaging zht
+ DO_2D(0,0,0,0)
+ zhu(ji,jj) = 0.5_wp * ( zht(ji,jj) + zht(ji+1,jj) )
+ END_2D
+ CALL lbc_lnk( 'usrdef_zgr', zhu, 'U', 1. ) ! boundary condition: this mask the surrouding grid-points
+ ! ! ==>>> set by hand non-zero value on first/last columns & rows
+ DO ji = mi0(1), mi1(1) ! first row of global domain only
+ zhu(ji,2) = zht(ji,2)
+ END DO
+ DO ji = mi0(jpiglo), mi1(jpiglo) ! last row of global domain only
+ zhu(ji,2) = zht(ji,2)
+ END DO
+ zhu(:,1) = zhu(:,2)
+ zhu(:,3) = zhu(:,2)
+ !
+ CALL zgr_z1d( pdept_1d, pdepw_1d, pe3t_1d , pe3w_1d ) ! Reference z-coordinate system
+ !
+ !
+ ! !== top masked level bathymetry ==! (all coordinates)
+ !
+ ! no ocean cavities : top ocean level is ONE, except over land
+ ! the ocean basin surrounded by land (1+nn_hls grid-points) set through lbc_lnk call
+ z2d(:,:) = 1._wp ! surface ocean is the 1st level
+ CALL lbc_lnk( 'usrdef_zgr', z2d, 'T', 1. ) ! closed basin, see userdef_nam.F90
+ k_top(:,:) = NINT( z2d(:,:) )
+ !
+ !
+ !
+ IF ( ld_sco ) THEN !== s-coordinate ==! (terrain-following coordinate)
+ !
+ k_bot(:,:) = jpkm1 * k_top(:,:) !* bottom ocean = jpk-1 (here use k_top as a land mask)
+ !
+ ! !* terrain-following coordinate with e3.(k)=cst)
+ ! ! ADIAB_WAVE case : identical with j-index (T=V, U=F)
+ z1_jpkm1 = 1._wp / REAL( jpkm1 , wp)
+ DO jk = 1, jpk
+ pdept(:,:,jk) = zht(:,:) * z1_jpkm1 * ( REAL( jk , wp ) - 0.5_wp )
+ pdepw(:,:,jk) = zht(:,:) * z1_jpkm1 * ( REAL( jk-1 , wp ) )
+ pe3t (:,:,jk) = zht(:,:) * z1_jpkm1
+ pe3u (:,:,jk) = zhu(:,:) * z1_jpkm1
+ pe3v (:,:,jk) = zht(:,:) * z1_jpkm1
+ pe3f (:,:,jk) = zhu(:,:) * z1_jpkm1
+ pe3w (:,:,jk) = zht(:,:) * z1_jpkm1
+ pe3uw(:,:,jk) = zhu(:,:) * z1_jpkm1
+ pe3vw(:,:,jk) = zht(:,:) * z1_jpkm1
+ END DO
+ ENDIF
+ !
+ !
+ IF ( ld_zco ) THEN !== z-coordinate ==! (step-like topography)
+ !
+ ! !* bottom ocean compute from the depth of grid-points
+ k_bot(:,:) = jpkm1 * k_top(:,:) ! here use k_top as a land mask
+ DO jk = 1, jpkm1
+ WHERE( pdept_1d(jk) < zht(:,:) .AND. zht(:,:) <= pdept_1d(jk+1) ) k_bot(:,:) = jk * k_top(:,:)
+ END DO
+ ! !* horizontally uniform coordinate (reference z-co everywhere)
+ DO jk = 1, jpk
+ pdept(:,:,jk) = pdept_1d(jk)
+ pdepw(:,:,jk) = pdepw_1d(jk)
+ pe3t (:,:,jk) = pe3t_1d (jk)
+ pe3u (:,:,jk) = pe3t_1d (jk)
+ pe3v (:,:,jk) = pe3t_1d (jk)
+ pe3f (:,:,jk) = pe3t_1d (jk)
+ pe3w (:,:,jk) = pe3w_1d (jk)
+ pe3uw(:,:,jk) = pe3w_1d (jk)
+ pe3vw(:,:,jk) = pe3w_1d (jk)
+ END DO
+ ENDIF
+ !
+ !
+ IF ( ld_zps ) THEN !== zps-coordinate ==! (partial bottom-steps)
+ !
+ ze3min = 0.1_wp * rn_dz
+ IF(lwp) WRITE(numout,*) ' minimum thickness of the partial cells = 10 % of e3 = ', ze3min
+ !
+ !
+ ! !* bottom ocean compute from the depth of grid-points
+ k_bot(:,:) = jpkm1
+ DO jk = jpkm1, 1, -1
+ WHERE( zht(:,:) < pdepw_1d(jk) + ze3min ) k_bot(:,:) = jk-1
+ END DO
+ !
+ ! !* vertical coordinate system
+ DO jk = 1, jpk ! initialization to the reference z-coordinate
+ pdept(:,:,jk) = pdept_1d(jk)
+ pdepw(:,:,jk) = pdepw_1d(jk)
+ pe3t (:,:,jk) = pe3t_1d (jk)
+ pe3u (:,:,jk) = pe3t_1d (jk)
+ pe3v (:,:,jk) = pe3t_1d (jk)
+ pe3f (:,:,jk) = pe3t_1d (jk)
+ pe3w (:,:,jk) = pe3w_1d (jk)
+ pe3uw(:,:,jk) = pe3w_1d (jk)
+ pe3vw(:,:,jk) = pe3w_1d (jk)
+ END DO
+ DO_2D( nn_hls, nn_hls, nn_hls, nn_hls )
+ ik = k_bot(ji,jj)
+ pdepw(ji,jj,ik+1) = MIN( zht(ji,jj) , pdepw_1d(ik+1) )
+ pe3t (ji,jj,ik ) = pdepw(ji,jj,ik+1) - pdepw(ji,jj,ik)
+ pe3t (ji,jj,ik+1) = pe3t (ji,jj,ik )
+ !
+ pdept(ji,jj,ik ) = pdepw(ji,jj,ik ) + pe3t (ji,jj,ik ) * 0.5_wp
+ pdept(ji,jj,ik+1) = pdepw(ji,jj,ik+1) + pe3t (ji,jj,ik+1) * 0.5_wp
+ pe3w (ji,jj,ik+1) = pdept(ji,jj,ik+1) - pdept(ji,jj,ik) ! = pe3t (ji,jj,ik )
+ pe3w (ji,jj,ik ) = pdept(ji,jj,ik ) - pdept(ji,jj,ik-1) ! st caution ik > 1
+ END_2D
+ ! ! bottom scale factors and depth at U-, V-, UW and VW-points
+ ! ! usually Computed as the minimum of neighbooring scale factors
+ pe3u (:,:,:) = pe3t(:,:,:) ! HERE configuration :
+ pe3v (:,:,:) = pe3t(:,:,:) ! e3 increases with i-index and identical with j-index
+ pe3f (:,:,:) = pe3t(:,:,:) ! so e3 minimum of (i,i+1) points is (i) point
+ pe3uw(:,:,:) = pe3w(:,:,:) ! in j-direction e3v=e3t and e3f=e3v
+ pe3vw(:,:,:) = pe3w(:,:,:) ! ==>> no need of lbc_lnk calls
+ !
+ ENDIF
+ !
+ END SUBROUTINE usr_def_zgr
+
+
+ SUBROUTINE zgr_z1d( pdept_1d, pdepw_1d, pe3t_1d , pe3w_1d ) ! 1D reference vertical coordinate
+ !!----------------------------------------------------------------------
+ !! *** ROUTINE zgr_z1d ***
+ !!
+ !! ** Purpose : set the depth of model levels and the resulting
+ !! vertical scale factors.
+ !!
+ !! ** Method : 1D z-coordinate system (use in all type of coordinate)
+ !! The depth of model levels is set from dep(k), an analytical function:
+ !! w-level: depw_1d = dep(k)
+ !! t-level: dept_1d = dep(k+0.5)
+ !! The scale factors are the discrete derivative of the depth:
+ !! e3w_1d(jk) = dk[ dept_1d ]
+ !! e3t_1d(jk) = dk[ depw_1d ]
+ !!
+ !! === Here constant vertical resolution ===
+ !!
+ !! ** Action : - pdept_1d, pdepw_1d : depth of T- and W-point (m)
+ !! - pe3t_1d , pe3w_1d : scale factors at T- and W-levels (m)
+ !!----------------------------------------------------------------------
+ REAL(wp), DIMENSION(:), INTENT(out) :: pdept_1d, pdepw_1d ! 1D grid-point depth [m]
+ REAL(wp), DIMENSION(:), INTENT(out) :: pe3t_1d , pe3w_1d ! 1D vertical scale factors [m]
+ !
+ INTEGER :: jk ! dummy loop indices
+ REAL(wp) :: zt, zw ! local scalar
+ !!----------------------------------------------------------------------
+ !
+ IF(lwp) THEN ! Parameter print
+ WRITE(numout,*)
+ WRITE(numout,*) ' zgr_z1d : Reference vertical z-coordinates: uniform dz = ', rn_dz
+ WRITE(numout,*) ' ~~~~~~~'
+ ENDIF
+ !
+ ! Reference z-coordinate (depth - scale factor at T- and W-points) ! Madec & Imbard 1996 function
+ ! ----------------------
+ DO jk = 1, jpk
+ zw = REAL( jk , wp )
+ zt = REAL( jk , wp ) + 0.5_wp
+ pdepw_1d(jk) = rn_dz * REAL( jk-1 , wp )
+ pdept_1d(jk) = rn_dz * ( REAL( jk-1 , wp ) + 0.5_wp )
+ pe3w_1d (jk) = rn_dz
+ pe3t_1d (jk) = rn_dz
+ END DO
+ !
+ IF(lwp) THEN ! control print
+ WRITE(numout,*)
+ WRITE(numout,*) ' Reference 1D z-coordinate depth and scale factors:'
+ WRITE(numout, "(9x,' level gdept_1d gdepw_1d e3t_1d e3w_1d ')" )
+ WRITE(numout, "(10x, i4, 4f9.2)" ) ( jk, pdept_1d(jk), pdepw_1d(jk), pe3t_1d(jk), pe3w_1d(jk), jk = 1, jpk )
+ ENDIF
+ !
+ END SUBROUTINE zgr_z1d
+
+ !!======================================================================
+END MODULE usrdef_zgr
diff --git a/tests/ADIAB_WAVE/README.md b/tests/ADIAB_WAVE/README.md
new file mode 100644
index 000000000..8c65fce56
--- /dev/null
+++ b/tests/ADIAB_WAVE/README.md
@@ -0,0 +1,55 @@
+# ADIAB_WAVE demonstration case
+
+We here provide a physical description of this experiment and additional details as to how to run this experiment within NEMO. This experiment is **created and tested** for NEMO **code at revision 15197**.
+An ipython notebook is also provided as a demonstration of possible analysis.
+
+## Objectives
+
+The purpose of this test case is to validate the implementation of the Generalized Lagrangian Mean equations for the coupling of NEMO with waves. This test case was first proposed by Ardhuin et al. (2008) and was successively detailed by Bennis et al (2011).
+
+## Physical description
+
+The ADIAB_WAVE test case consists on a steady monochromatic wave shoaling on a slope from 4 to 6-meter depth. It does not take into account the wave breaking nor bottom friction nor wave-induced mixing. The domain is rectangular with a size of 780m x 50m and with a uniform horizontal grid spacing rn_dx=rn_dy=10m. The flow is confined to a channel with a free-slip boundary condition at North and South and the bottom topography is uniform in the y-direction. The vertical grid is a terrain-following sigma-coordinates with 120 layers. The domain is initialized at rest with a constant temperature and salinity.
+
+In a sake of simplicity, for this testcase, NEMO is forced offline by the wave model WaveWatch3 (WW3).
+The initial characteristics of the wave field are a significant wave height Hs=1.02m, a wave period T=5.26s, and a wave direction theta=90° (propagation in the x-direction).
+
+The calculation of the Stokes drift vertical decay in NEMO is currently based on the deep water approximation that is not verified in this test case. The MY_SRC/sbcwave.F90 includes the Stokes drift vertical decay parameterization for intermediate/shallow-water (Michaud et al, 2012).
+
+## Exemple of run
+
+The calculation of the Stokes drift for intermediate water is activated by the option ln_STOKES_ADIAB= .TRUE. in the namusr_def section of the namelist.
+
+~~~fortran
+!-----------------------------------------------------------------------
+&namusr_def ! User defined : ADIAB_WAVE configuration
+!-----------------------------------------------------------------------
+ ! ! type of vertical coordinate
+ ln_zco = .false. ! z-coordinate
+ ln_zps = .false. ! z-partial-step coordinate
+ ln_sco = .true. ! s-coordinate
+ rn_dx = 10. ! horizontal resolution [meters]
+ rn_dy = 10.
+ rn_dz = 0.05 ! vertical resolution [meters]
+ ln_STOKES_ADIAB = .true. ! Stokes Drift (Shallow/Intermediate water)
+ /
+~~~
+
+The input_data folder contains the wave forcing as well as the East and West open boundary forcing.
+The wave forcing file (sdw_WW3_ADIABWAVE_y0001m01d01.nc.tar.gz) need to be uncompressed before running.
+
+Run the executable :
+```
+mpirun -np 1 ./nemo
+```
+
+The ADIAB_GLM_grid_U.nc output file should be generated.
+
+The jupyter-notebook adiabwave_notebook.ipynb is set up to generate the vertical cross-section plots of the Stokes drift, the quasi-Eulerian velocity as well as the Lagrangian velocity after 900s of simulation. The quasi-Eulerian velocity is irrotational and homogeneous over the water column. The Lagrangian velocity vertical shear is entirely due to the Stokes drift.
+
+## References
+Ardhuin, F., Rascle, N., Belibassakis, K.A., 2008. Explicit wave-averaged primitive equations using a generalized Lagrangian mean. Ocean Model. 20, 35–60.
+
+Bennis, A.C., Ardhuin, F., Dumas, F., 2011. On the coupling of wave and three-dimensional circulation models: Choice of theoretical framework, practical implementation and adiabatic tests. Ocean Model. 40, 260–272.
+
+Michaud, H., Marsaleix, Leredde, P., Estournel, C., Bourrin, F., Lyard, F., Mayet, C., Ardhuin, F., 2012. Three-dimensional modelling of wave-induced current from the surfzone to the inner shelf. Ocean Sci. 8, 657–681.
diff --git a/tests/ADIAB_WAVE/cpp_ADIAB_WAVE.fcm b/tests/ADIAB_WAVE/cpp_ADIAB_WAVE.fcm
new file mode 100644
index 000000000..a47f2e69c
--- /dev/null
+++ b/tests/ADIAB_WAVE/cpp_ADIAB_WAVE.fcm
@@ -0,0 +1 @@
+ bld::tool::fppkeys key_xios
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diff --git a/tests/ADIAB_WAVE/notebook/adiabwave_notebook.ipynb b/tests/ADIAB_WAVE/notebook/adiabwave_notebook.ipynb
new file mode 100644
index 000000000..1ae306261
--- /dev/null
+++ b/tests/ADIAB_WAVE/notebook/adiabwave_notebook.ipynb
@@ -0,0 +1,288 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "# ADIAB_WAVE TESTCASE\n",
+ "\n",
+ "This notebook shows how to plot the vertical cross-section of the Stokes drift, the quasi-Eulerian velocity and the Lagrangian velocity for the ADIAB_WAVE test case experiment. This allow you to compare your results with Ardhuin et al (2008) and Bennis et al. (2011). The results exhibit a vertical shear in the Lagrangian velocity that is entirely due to the Stokes drift. The quasi-Eulerian velocity is irrotational and homogeneous over the water column. The Stokes drift divergence is compensated by a strong convergence in the quasi-Eulerian velocity.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ ],
+ "source": [
+ "import netCDF4 as nc4\n",
+ "import numpy as np\n",
+ "from netCDF4 import Dataset\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.cm as cm\n",
+ "font = {'weight' : 'bold',\n",
+ " 'size' : 18}\n",
+ "plt.rc('font', **font)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# netcdf file\n",
+ "exp_name = 'EXP00'\n",
+ "file_name = f'../{exp_name}/ADIAB_GLM_grid_U.nc'\n",
+ "tind = 899\n",
+ "ncfile = Dataset(file_name, mode='r')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# read variables\n",
+ "depth=ncfile.variables['e3u_inst'][tind,0:119,2,1:76].squeeze()\n",
+ "depth=np.cumsum(depth,0)\n",
+ "nav=ncfile.variables['nav_lon'][2, 1:76]\n",
+ "nav=np.tile(nav,(np.size(depth,0),1))\n",
+ "us=ncfile.variables['sdzocrtx'][tind, 0:119 ,2 , 1:76]\n",
+ "u=ncfile.variables['uo_inst'][tind, 0:119 ,2 , 1:76]\n",
+ "ncfile.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 864x432 with 2 Axes>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {
+ "image/png": {
+ "height": 408,
+ "width": 710
+ },
+ "needs_background": "light"
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Figure Stokes drift (Us)\n",
+ "fig, ax = plt.subplots(figsize=(12,6))\n",
+ "level=np.arange(0.006,0.054, 0.002 )\n",
+ "CS = ax.contourf(nav,depth,us,level)\n",
+ "cbar=fig.colorbar(CS)\n",
+ "cbar.ax.tick_params(labelsize=20)\n",
+ "ax.set_title('Stokes drift (m/s) after t=%s s'%(tind+1))\n",
+ "ax.invert_yaxis()\n",
+ "plt.ylabel('depth(m)')\n",
+ "plt.xlabel('x(km)')\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 864x432 with 2 Axes>"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {
+ "image/png": {
+ "height": 408,
+ "width": 727
+ },
+ "needs_background": "light"
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Figure quasi-Eulerian velocity (U)\n",
+ "fig, ax = plt.subplots(figsize=(12,6))\n",
+ "CS = ax.contourf(nav,depth,u)\n",
+ "cbar=fig.colorbar(CS)\n",
+ "cbar.ax.tick_params(labelsize=20)\n",
+ "ax.set_title('quasi-Eulerian velocity (m/s) after t=%s s'%(tind+1))\n",
+ "ax.invert_yaxis()\n",
+ "plt.ylabel('depth(m)')\n",
+ "plt.xlabel('x(km)')\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 864x432 with 2 Axes>"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {
+ "image/png": {
+ "height": 408,
+ "width": 727
+ },
+ "needs_background": "light"
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Figure Lagrangian velocity (UL=U+Us)\n",
+ "fig, ax = plt.subplots(figsize=(12,6))\n",
+ "CS = ax.contourf(nav,depth,u+us,10)\n",
+ "cbar=fig.colorbar(CS)\n",
+ "cbar.ax.tick_params(labelsize=20)\n",
+ "ax.set_title('Lagrangian velocity(m/s) after t=%s s'%(tind+1))\n",
+ "ax.invert_yaxis()\n",
+ "plt.ylabel('depth(m)')\n",
+ "plt.xlabel('x(km)')\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (Anaconda 2020)",
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+ "JAVA_HOME": "/ext/anaconda2020.02",
+ "JAVA_HOME_CONDA_BACKUP": "",
+ "JAVA_LD_LIBRARY_PATH": "/ext/anaconda2020.02/lib/server",
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+ "LD_GOLD": "/ext/anaconda2020.02/bin/x86_64-conda_cos6-linux-gnu-ld.gold",
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+ "PROJ_LIB": "/ext/anaconda2020.02/share/proj",
+ "RANLIB": "/ext/anaconda2020.02/bin/x86_64-conda_cos6-linux-gnu-ranlib",
+ "READELF": "/ext/anaconda2020.02/bin/x86_64-conda_cos6-linux-gnu-readelf",
+ "RSTUDIO_WHICH_R": "/ext/anaconda2020.02/bin/R",
+ "SIZE": "/ext/anaconda2020.02/bin/x86_64-conda_cos6-linux-gnu-size",
+ "STRINGS": "/ext/anaconda2020.02/bin/x86_64-conda_cos6-linux-gnu-strings",
+ "STRIP": "/ext/anaconda2020.02/bin/x86_64-conda_cos6-linux-gnu-strip",
+ "_CE_CONDA": "",
+ "_CE_M": "",
+ "_CONDA_PYTHON_SYSCONFIGDATA_NAME": "_sysconfigdata_x86_64_conda_cos6_linux_gnu"
+ },
+ "language": "python",
+ "metadata": {
+ "cocalc": {
+ "description": "Python/R distribution for data science",
+ "priority": 5,
+ "url": "https://www.anaconda.com/distribution/"
+ }
+ },
+ "name": "anaconda2020",
+ "resource_dir": "/ext/jupyter/kernels/anaconda2020"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--
GitLab