# 4. Post-processing and plotting FAQ

## 4.1. NorESM2-LM CMIP6 experiments with different physics

**Q:** For many of the RFMIP and AerChemMIP (CMIP6) simulations with NorESM2-LM, two different simulations (i.e. rxi1p1f1 and rxi1p2f1) are available on ESGF. Why?

**A:** Please see CMIP6 archive of NorESM results for context and advice.

### 4.1.1. Emission-driven compsets

**Q:** How do I run NorESM2 in an emission-driven mode with an interactive carbon-cycle?

**A:** Please see Experiments for recommendations.

## 4.2. Different sea-ice and ocean grid

**Q:** The sea ice output variables in NorESM2 are on a 360x384 grid, while the ocean output variables are on a 360x385 grid. Which variable shall I use if I want to e.g. calculate the area sum of the sea ice (e.g., sea ice volume in the Northern Hemisphere)?

**A:**
The ocean/sea-ice grid of NorESM2 is a tripolar grid with 360 and 384 unique grid cells in i- and j-direction, respectively. Due to the way variables are staggered in the ocean model, an additional j-row is required explaining the 385 grid cells in the j-direction for the ocean grid. The row with j=385 is a duplicate of the row with j=384, but with reverse i-index.

The ocean and sea-ice components of NorESM define the grid cell area differently. In the ocean component, the grid cell area is found by computing the area of a spherical polygon with grid cell corners as vertices. The sea-ice component computes the area as dx*dy where dx and dy are grid cell sizes in i- and j-direction, respectively. In order to achieve good conservation in flux exchanges, we ensure that the ocean and sea-ice components have identical grid cell areas. To obtain this with the different approaches of computing grid cell area, we nudge the sea-ice grid locations slightly.

In conclusion, it is consistent to use the area variable defined on the ocean grid in relation to sea-ice variables, but you have to ignore the final j-row of e.g. area. So to conclude, just drop the last row with j=385 of area when dealing with the sea ice variables.

## 4.3. Very large ocean cell thickness in BLOM (the ocean component in NorESM2)

The ocean layer thickness **dz** variable may not be very meaningful for NorESM. The ocean model component BLOM is an isopycnic-coordinated model and hence the model layer thickness is changing from each integration step. Therefore, it is possible that the layer thickness will exceed 3km to 4km under certain circumstances. For example, this occurs sometimes in polar waters under deep convection where the ocean column is not stratified. And also at some coastal regions (where the water may not be well-represented by ‘isopycnic’ movement). So in short, **dz** reflects how the model represents the water masses (faithfully or not).

## 4.4. Weights and area information for the ocean component BLOM

The area and mask information for BLOM output can be found in the grid file usually stored together with the input data used by BLOM. Please see http://ns9560k.web.sigma2.no/inputdata/ocn/blom/grid/ for the grid files used.

Weights for ocean calculations:

```
gridpath = 'path_to_gridfile' # path to grid files
grid = xr.open_mfdataset(gridpath + 'grid_tnx1v4_20170622.nc')
parea = grid.parea
pmask = grid.pmask
pweight = parea*pmask
```

## 4.5. The vertical coordinate in BLOM

**Q:**
The vertical coordinate of NorESM2 is provided as the isopycnal coordinate (kg/m^3). I want to change this isopycnal coordinate to z coordinate (m).

**A:**
Vertically pre-interpolated output to z-level (including temperature, salinity and the overturning mass stream-functions) should be available for all NorESM2 experiments. For raw model output these variables often end with *lvl* . E.g.

Temperature: templvl(time, depth, y, x)

Salinity: salnlvl(time, depth, y, x)

Velocity x-component: uvellvl(time, depth, y, x)

Velocity y-component: vvellvl(time, depth, y, x)

Overturning stream-function: mmflxd(time, region, depth, lat)

For CMORIZED data the pre-interpolated output to z-level uses a different grid identifier than *gn* (grid native). Please note that *gr* usually means regridded horizontally but in case of NorESM2 it is regridded vertically. E.g.

Temperature: thetao(time, depth, y, x) on

*gr*gridSalinity: so(time, depth, y, x) on

*gr*gridVelocity x-component: uo(time, depth, y, x) on

*gr*gridVelocity y-component: vo(time, depth, y, x) on

*gr*gridOverturning stream-function: msftmz(time, region, depth, lat) on

*grz*grid

## 4.6. The surface variables in BLOM

**Q:** Are the surface variables diagnosed in BLOM identical to the values in the upper (“surface”) layer (e.g. sst compared to temp @sigma =27.22 and templvl @depth = 0m)?

**A:** Usually not. So if you think “surface is surface”, please read below:

The surface mixed boundary layer in BLOM is divided into 2 model layers with thickness dz(1) and dz(2) for the upper and lower layer, respectively. Let h = dz(1) + dz(2) be the total thickness of the mixed layer, then dz(1) = min(10 m, h/2). Further, the minimum thickness of the mixed layer is 5 m. Thus, the upper model layer, dz(1), will have a thickness between 2.5 m to 10 m. For a comparison of the output variables **sst**, **temp**, **templvl** :

temp:the temperature weighted by the thickness of the layer. For the upper layer this will be:sum(temp(1)*dz(1))/sum(dz(1))

time averaged over the time interval used for the diagnostics.

templvl: the temperature weighted by a pre-defined depth interval for every time step and subsequently averaged over the time interval used for the diagnostics. For the upper (first) layer of templvl, the depth interval is 0 to 5 m.

sst: temperature in the upper (first) model layer for every time step in the diagnostics interval and subsequently averaged over the time interval used for the diagnostics.

Thus:

tempandsstwill usually not be identical sincetempis weighted by the layer thickness andsstis not. The only exception is if h is greater than 20m throughout the average time period used for the diagnostics, then a constant weighting will be applied (i.e. dz(1) = 10 m).

templvlandsstwill usually not be identical sincetemplvlis weighted by the layer depth interval andsstis not. The only exception is if dz(1) is greater then 5 m throughout the average time period used for the diagnostics. Usually, dz(1) is less than 5 m in some regions e.g. tropical upwellilng regions and hence templvl @depth=0 and sst will differ.

These results apply to other variables as well (e.g. salinity and velocities) and to all CMIP6 compsets. Please note, for the actual weighting calculations in BLOM pressure is used instead of layer thickness, but the explanation stays the same.

## 4.7. What is the ocean density value used to convert from kg/s to Sverdrups?

If you would like to be exact it would be the local density (which you could calculate based on T,S properties), but you can just use **1000 kg/m3** (i.e. just divide by 1E9 to get transport in Sverdrups). The ocean model in NorESM (BLOM) is actually mass conserving, so the mass flux is the *real* flux that the model uses and the volume flux is more of a diagnostic quantity. In models that are volume conserving (most CMIP models) the volume flux is what the model uses and they diagnose mass flux by multiplying with constant density, which is not what is done in NorESM.

## 4.8. Why is the depth-integrated vertical transport not equal to zero?

There is no need for the total (integrated) vertical transport across depth to be zero, and it is also not very meaningful to calculate the integral: it is the integral of horizontal+vertical convergence that should be close to zero (but also that does not need to be exactly zero, because the sea level can change at monthly timescale).

Note that the vertical transport (wmo) is defined at level (or the model layer coordinate if you use gn-grid) interfaces, so you can check the vertical convergence for example wmo.diff(‘lev’).sum(‘lev’) which will be much closer to zero - if you take into account the horizontal convergence the closure will be much better, although things probably won’t exactly close using the monthly output. If you just want to check the conservation, it is better to use the layer coordinate (gn, with vertical coordinate ‘rho’).

## 4.9. Is there a tool for creating time series files from the raw NorESM output?

Yes, there is! PyReshaper is a post-processing tool developed for the CESM that can be used to convert raw (time-slice) output to time series (one file per variable).

The code is available here: https://github.com/NCAR/PyReshaper

The documentation is available here: https://ncar.github.io/PyReshaper/index.html

If you are a NIRD user and a member of the INES Unix group, you can use the PyReshaper installation available under /projects/NS9560K/pyreshaper/ (see README file in that folder for details).

## 4.10. How do I fix the time issue in monthly files (h0-files)?:

The monthly files in NorESM2 (not BLOM/MICOM/iHAMOCC files) are written *after* the last time step of the month. Consequently, the date in the netcdf file is the first of the following month. E.g. The date in FILENAME.cam.h0.0001-01.nc will be 01-02-0001 (the first of *February* and not January). This needs to be taken into account when calculating annual averages using python packages like xarray and iris. One method is to use the time bounds (instead of time), another method is to correct the time stamps in the time array. If the time variable is not corrected, none of the python functions involving time e.g. yearly averages, seasonal averages etc. will provide correct information

**xarray**

```
def fix_cam_time(ds):
'''
Parameters
----------
ds : xarray.Dataset
Returns
-------
ds : xarray.Dataset with corrected time
'''
from cftime import DatetimeNoLeap
months = ds.time_bnds.isel(bnds=0).dt.month.values
years = ds.time_bnds.isel(bnds=0).dt.year.values
dates = [DatetimeNoLeap(year, month, 15) for year, month in zip(years, months) ]
ds = ds.assign_coords(time = dates)
return ds
```

**iris**

```
def subtract_second_timedim(cube):
'''
Fix time issue by subtracting one second from the time array
'''
time = cube.coord("time")
new_points = time.points - 1/86400
new_time = DimCoord(new_points, standard_name="time",
units=time.units)
cube.remove_coord("time")
cube.add_dim_coord(new_time, 0)
return cube
```

## 4.11. How do I compute a weighted average?

**Using NCL**

Examples on how to compute and plot weighted averages: https://www.ncl.ucar.edu/Applications/ave.shtml

See also the examples at the bottom of the documentation for the ncl function wgt_areaavg (which computes the weighted average): https://www.ncl.ucar.edu/Document/Functions/Built-in/wgt_areaave.shtml

**Using python**

When calculating annual averages from NorESM2 data it is important use appropriate monthly weights, especially for individual radiative fluxes (can have errors of the order of 0.5-1 W/m^2 if not used). Please remember to fix the time issue in the monthly cam and clm files (see the previous question).

**xarray**

For BLOM/MICOM/iHAMOCC files there are no issues with the time variable, and annual averages can be calculated:

```
def annual_mean_to_file(var,fname,weights=np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])/365):
'''
Calculate annual means from monthly means assuming no-leap calendar
'''
month_weights = xr.DataArray(np.tile(weights,len(var.time)//12),coords=[var.time], name='month_weights')
annual_mean = (month_weights*var).groupby('time.year').sum('time')
annual_mean = annual_mean.rename({'year':'time'})
annual_mean = annual_mean.where(annual_mean!=0)
annual_mean.rename(var.name).to_dataset().to_netcdf(fname)
```

One way to handle the time issue is to take annual averages by looping over 12 files at the time (slow method):

```
def area_avg(ds, var, monthw = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])):
'''
Calculate global and annual means from monthly means
'''
field = ds[var].mean(dim = 'lon')
return np.sum(monthw*[ np.nansum((field[i,:]*ds.gw[0]).values)/
np.nansum(ds.gw[0]) for i in range(0,len(ds[var].time))])/np.sum(monthw)
```

Weights for ocean calculations:

```
gridpath = 'ocngrid/tnx1v4/' # path to grid files
grid = xr.open_mfdataset(gridpath + 'grid.nc')
parea = grid.parea
pmask = grid.pmask
pweight = parea*pmask
```

**iris**

It is also possible to use iris for analysing and visualising NorESM2 data Documentation: https://scitools.org.uk/iris/docs/latest/

```
def get_cube_varname(cube_list, var_name):
'''
Subtract cube with name var_name from the cube_list
'''
if type(var_name) is list:
var_cube = iris.cube.CubeList()
for name in var_name:
print(name)
for cube in cube_list:
if cube.var_name == name:
var_cube.append(cube)
return sum(var_cube)
else:
for cube in cube_list:
if cube.var_name == var_name:
return cube
def annual_weighted_avg(path,file, varname):
'''
Calculate global and annual means from monthly means
'''
cube = iris.load(path + file)
ts = get_cube_varname(cube, varname)
cube = subtract_second_timedim(ts)
lons = cube.coord("longitude")
lats = cube.coord("latitude")
lons.guess_bounds()
lats.guess_bounds()
weights = iris.analysis.cartography.area_weights(cube)
cube_collapsed =cube.collapsed(coords=["latitude", "longitude"],
aggregator=iris.analysis.MEAN,
weights=weights)
monthw=[31,28,31,30,31,30,31,31,30,31,30,31]
monthw = np.tile(monthw, 30)
monthw=monthw/np.sum(monthw)
n=len(monthw)
tmp = [cube_collapsed[i:i+n].collapsed('time', aggregator= iris.analysis.MEAN,weights=monthw) for i in range(0,n*yrs,n)]
cubes_aa = iris.cube.CubeList(tmp).merge()
return cubes_aa[0]
```