ECMWF COPERNICUS REPORT

Copernicus Climate Change Service

Documentation of the METEO-FRANCE
Pre-Operational seasonal forecasting
systemIssued by:
Date: 12/01/2017
Ref: SERVICE CONTRACT N° 2015/C3S_433_Lot1METEO-FRANCE (Deliverable D3.1)

Copernicus Atmosphere Monitoring Service

This document has been produced in the context of the Copernicus Climate Change Service (C3S).
The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts,
operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014). All information in thisdocument is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.
The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission
and the European Centre for Medium-Range Weather Forecasts has no liability in respect of this document, which is merely
representing the authors view.

Copernicus Atmosphere Monitoring Service

Documentation of the METEO-FRANCE
Pre-Operational seasonal forecasting
systemMETEO-FRANCE
Laurent Dorel
Constantin Ardilouze
Michel Déqué
Lauriane BattéJean-François Guérémy

Date: 12/01/2017
REF.: SERVICE CONTRACT N° 2015/C3S_433_Lot1METEO-FRANCE (Deliverable D3.1)

Contents
1 Basic facts ............................................................................................................. 3
2. Modeling components of the forecast system ...................................................... 7
2.1 Introduction ........................................................................................................................... 7
2.2 The atmospheric model, Arpege V6.2.5 ................................................................................... 8
2.3 The ocean model Nemo V3.6 and sea-ice model Gelato V6 ..................................................... 9
2.4 The Land Surface model Surfex V8.1 and river routing model TRIP ........................................ 10
2.5 The coupler Oasis MCT v3.0 .................................................................................................. 11
2.6 Eclis ...................................................................................................................................... 12

3. Generation of stochastic perturbations .............................................................. 14
3.1 Introduction ......................................................................................................................... 14
3.2 The nudged pre-hindcasts: generating the perturbation fields............................................... 14
3.3 Random calendar ................................................................................................................. 17
3.4 Definition of the ensembles .................................................................................................. 19
3.5 Model implementation ......................................................................................................... 19

4. Generation of initial conditions .......................................................................... 22
4.1 Fetching the initial conditions (hindcast) ............................................................................... 23
4.2 Fetching the initial conditions (forecast) ............................................................................... 25

5. Environment for CLImate Simulation (Eclis) ........................................................ 26
5.1 Eclis organization .................................................................................................................. 26
5.2 Use of Eclis ........................................................................................................................... 26
5.3 Design a ’param_’ file ........................................................................................................... 27
5.4 Standard plugins................................................................................................................... 27

6 The basic set of scripts ........................................................................................ 28
7. Post processing .................................................................................................. 29

Deliverable D3.1

1 of 32

12/01/2017

Copernicus Climate Change Service

Introduction
Météo-France built its own model for numerical weather prediction as soon as theearly 1970's, and for climate simulations in the early 1980's. However, seasonal
forecasting became a fully operational activity in the late 1990's. The seasonal
forecast system at Météo-France, imbedded in the Eurosip consortium, has evolved
by successive versions, named “systems” as they include modifications of several
models as well as initial conditions. A new system is issued every 3-4 years. The
presently operational system is Eurosip system 5. This 2-year old forecast system isused in the proof of concept phase of the C3S-433 contract. During the first year of
the contract, we have assembled different components to build up a new forecast
system which will be used in the pre-operational phase.
After a brief overview of the new system, the present document describes thefeatures of its the individual components. In a second part, more technical, thedocument details the first stage of a forecast, consisting in building initial states forthe atmosphere, ocean, sea-ice and land surface. It also features the preliminary
operations which generate the stochastic perturbations and the hindcast ensembles.
The procedure managing the forecasts (and the hindcasts) is then detailed. Finally,
the post-processing of raw output files from the model integration into standardized
time series and monthly-mean files is shortly described.
This document does not address the atmosphere and ocean data assimilation
techniques which are carried out respectively by ECMWF and MERCATOR-OCEAN.

Deliverable D3.1

2 of 32

12/01/2017

Copernicus Climate Change Service

1 Basic facts
The table below gives the main features of the pre operational forecast system.

Table 1 : basic facts

PO forecast system
Ensemble version
Ensemble version identifier code

CNRM-CM 6

Short description

Global ensemble system using a laggedaverage and a stochastic scheme to take
into account initial state and model
uncertainties. Based on 51 members, run
once a month up to 7 months.

Status

Pre-Operational

Data time of the first forecast run

01/03/2017

Configuration of the EPS
Is the model is coupled to an ocean
model ?

Yes from day 0

Short description of the ocean model

Nemo v3.6 , ORCA 1° grid, 75 modellevelsIs the model is coupled to a sea ice model Yes?
Short description of the sea ice modelSea-ice model is Gelato v6 (Salas y Melia,2002), embedded in the ocean model. It
is initialized from unperturbed 1 degree
resolution MERCATOR-OCEAN Ocean and
Sea-Ice Analysis

Is the model is coupled to a wave model ? No
Horizontal resolution of the atmospheric TL359
model
Number of model levels

91

Top of the model

0.01 hPa

Type of model levels

Hybrid sigma-pressure

Deliverable D3.1

3 of 32

12/01/2017

Copernicus Climate Change Service

Forecast length

7 months

Run frequency

Once a month

Is there an unperturbed control forecast
includedNo

Number of perturbed ensemble members 51
Integration time step

Atmosphere/surface: 7'30''
Ocean/Sea-Ice: 30'
Coupling frequency: 3 hours

Initial conditions and perturbations
Data assimilation method for control
analysis

4D VarResolution of the model used to generate O1280L137 (IFS operational analysis)
Control Analysis
Ensemble initial perturbation strategy

Lagged-average and in-run perturbations

Model uncertainties perturbations
Is model physics perturbed ?

No

Do all ensemble members use exactly the Yessame model version ?
Is model dynamics perturbed ?

Yes (Batté and Déqué 2016)

Are the above perturbations applied tothe control forecast ?

Yes

Surface boundary perturbations
Perturbation to sea surface temperature No?
Perturbation to soil moisture ?

No

Perturbation to surface stress orroughness ?

No

Any other surface perturbation ?

No

Other details of the models
Description of the model grid

Deliverable D3.1

Reduced Gaussian Grid

4 of 32

12/01/2017

Copernicus Climate Change Service

List of model levels

From top to bottom (Pa) 1, 3, 6, 10, 17,
27, 42, 63, 92, 129, 177, 237, 311, 401,
508, 633, 779, 946, 1136, 1349, 1587,
1850, 2139, 2454, 2796, 3165, 3560,
3982, 4430, 4904, 5403, 5927, 6475,
7046, 7638, 8252, 8890, 9555, 10251,
10982, 11752, 12566, 13431, 14350,
15326, 16362, 17461, 18628, 19865,
21176, 22565, 24037, 25595, 27245,
28990, 30836, 32788, 34851, 37031,
39333, 41763, 44324, 47007, 49787,
52637, 55531, 58441, 61342, 64222,
67067, 69864, 72601, 75262, 77836,
80312, 82678, 84922, 87035, 89011,
90842, 92520, 94042, 95408, 96616,
97666, 98562, 99329, 99983, 100512,
100914, 101204

What kind of large scale dynamics is used Spectral semi-lagrangian?
What kind of boundary layer
parametrization is used ?

Cuxart, Bougeault and Redelsperger(2000)

What kind of convective parametrization Gueremy (2011), Piriou et al. (2007)is used ?
What kind of large scale precipitation
scheme is used ?

Lopez (2002)

What cloud scheme is used ?

Sommeria and Deardorff (1977)

What kind of land-surface scheme is used Explicit multilayer snow and soil scheme?
as described in Masson et al. (2013)
How is radiation parametrized ?

Long wave radiation : Mlawer et al.
(1997)
Short wave radiation : Morcrette (1990)

Re-forecast configuration
Number of years covered

23 years (1993-2015)

Produced on the fly or fix re-forecasts ?

fix re-forecasts

Deliverable D3.1

5 of 32

12/01/2017

Copernicus Climate Change Service

Frequency

monthly

Ensemble size

25 members

Initial conditions

ERA Interim (T255L60) for atmosphericand land surface
MERCATOR-OCEAN reanalyses for ocean

Is the model physics and resolution thesame as for the real-time forecasts ?

Yes

Is the ensemble generation the same as
for the real-time forecasts ?

Yes

Deliverable D3.1

6 of 32

12/01/2017

Copernicus Climate Change Service

2. Modeling components of the forecast system
2.1 Introduction

As any long-range forecast system, Météo-France system consists in an ensemble
forecast operational production together with an ensemble re-forecast dataset, also
called hindcast.
Both ensembles come from integrations of the global coupledatmosphere/ocean/sea-ice/land-surface model CNRM-CM illustrated in Figure 1.
CNRM-CM runs at Météo-France on the beaufix and prolix supercomputers.

Figure 1 - CNRM-CM model components of the seasonal forecast system

Deliverable D3.1

7 of 32

12/01/2017

Copernicus Climate Change Service

2.2 The atmospheric model, Arpege V6.2.5

Arpege-IFS forecast model has been jointly developped by ECMWF and Météo-France
for numerical weather prediction in the late 1980s (Courtier and Geleyn, 1988). A
climate version of this model, especially designed for climate change and seasonal
predictability experiments by Déqué et al. (1994). The weather and climate versions
have evolved in parallel since this date, including major improvements like semilagrangian advection and high performance parallelization. As far as theparameterization packages are concerned, ECMWF and Météo-France use different
schemes, except for radiation. As far as the climate version is concerned, the majorchange with respect to the version used in system 5 (used in CMIP5 as well) is the
adoption of a fully new set of physical parameterizations prepared for the next CMIP6
experiment.
The physics used in system 5 and the previous systems back to the Demeter project isnamed “diagnostic physics”. This set of physical parametrizations was based on thelaws of thermodynamics and of statistical mechanics. The main assumption was thatthe diabatic processes reached a statistical equilibrium for each grid point at eachtime step. This assumption is reasonable when the time step is 30 min or longer orwhen the grid size is 100 km or larger. However, the limitations of the “diagnostic
physics” appeared when used for short-range weather prediction or in a 1dimensional model driven by observations: the time step by time step evolution ischaotic. This is because in this approach each time step ignores the diabatic
calculations of the previous one. One of the most noticeable consequence was thefact that the daily cycle of convective precipitation reached its maximum at noon.
When considering monthly averages, the model chaotic behaviour is not thatdetrimental. This explains why Météo-France used the “diagnostic physics” in CMIP5
scenarios as well as is Eurosip system 5, while developing a new physics.

The new “prognostic physics” relies upon the same assumption that the diabatic
terms are calculated for all atmospheric columns independently. But it includes a

Deliverable D3.1

8 of 32

12/01/2017

Copernicus Climate Change Service

memory and a horizontal exchange of the diabatic processes through new prognostic
variables: turbulent kinetic energy, convective vertical velocity, solid and liquid waterin different states (cloud, large-scale precipitation and convection). These variables
are advected and memorized along the whole simulation. Of course theparametrizations of convection (Guérémy, 2011), of turbulence (Cuxart et al., 2000)
and of cloud and large-scale precipitation (Lopez, 2002) have been re-written to take
into account the time evolution of these variables.

The time step has been reduced to improve the short time evolution of the fluxes: 1h
for radiation and 7.5 min for the rest of the physics, instead of 3h and 15 min in theformer version.

A detailed presentation of Arpege equations is available here:
http://www.umr-cnrm.fr/spip.php?article124&lang=en
http://www.umr-cnrm.fr/IMG/pdf/arp62ca.pdf

2.3 The ocean model Nemo V3.6 and sea-ice model Gelato V6

The ocean engine of Nemo (Nucleus for European Modelling of the Ocean) is aprimitive equation model adapted to regional and global ocean circulation problems.
It is intended to be a flexible tool for studying the ocean and its interactions with theothers components of the earth climate system over a wide range of space and timescales. Prognostic variables are the three-dimensional velocity field, a non-linear sea
surface height, the Conservative Temperature and the Absolute Salinity. In thehorizontal direction, the model uses a curvilinear orthogonal grid and in the verticaldirection, a full or partial step z -coordinate, or s -coordinate, or a mixture of the two.
The distribution of variables is a three-dimensional Arakawa C-type grid. Various
physical choices are available to describe ocean physics, including TKE, and GLS
vertical physics. Within Nemo, the ocean is interfaced with a sea-ice model (Gelato
V6), and, via the Oasis coupler, with the atmospheric model Arpege V6.2.5.

Deliverable D3.1

9 of 32

12/01/2017

Copernicus Climate Change Service

A more detailed presentation of Nemo is available here:
http://www.nemo-ocean.eu/About-NEMO/Reference-manuals

The reference paper for the current version of the model Gelato is Salas y Mélia(2002).

2.4 The Land Surface model Surfex V8.1 and river routing model TRIP

Surfex (Surface Externalisée, in French) is a surface modelling platform developed by
Météo-France in cooperation with the scientific community.
Surfex is composed of various physical models for natural land surface, urbanizedareas, lakes and oceans. It also simulates chemistry and aerosols surface processes
and can be used for assimilation of surface and near surface variables.
Surfex has its own initialisation procedures and can be used in stand alone mode andcoupled to an atmospheric model.
In Surfex, each model grid box is represented by four surface types : sea or ocean,water bodies (lakes, ...), urban areas and nature (soil and vegetation). Each surface
type is modelled with a specific surface model and the total flux of the grid box
results from the addition of the individual fluxes weighted by their respective
fraction.

A more detailed presentation of Surfex is available here:
http://www.umr-cnrm.fr/surfex/
The reference paper for the last version of Surfex is in preparation while writing thisdocument. However, most novel features have been published in Le Moigne et al.
(2016), Decharme et al. (2015), Vergne et al. (2014), Decharme et al. (2013) and
Decharme et al. (2012).

Deliverable D3.1

10 of 32

12/01/2017

Copernicus Climate Change Service

2.5 The coupler Oasis MCT v3.0

Oasis is the coupler that manages the exchange of information between the three
aforementioned model groups.

In 1991, CERFACS started the development of a software interface to couple existing
ocean and atmosphere numerical General Circulation Models. Today, different
versions of the Oasis coupler are used by about 45 modelling groups all around theworld on different computing platforms1. Oasis sustained development is ensured bya collaboration between CERFACS and the Centre National de la Recherche
Scientifique (CNRS).

The current OASIS3-MCT internally uses MCT, the Model Coupling Toolkit2 (Larson etal 2005) (Jacob et al 2005), developed by the Argonne National Laboratory in the USA.
MCT implements fully parallel regridding, as a parallel matrix vector multiplication,and parallel distributed exchanges of the coupling fields, based on pre-computed
regridding weights and addresses. Its design philosophy, based on flexibility andminimal invasiveness, is close to the Oasis approach. MCT has proven parallel
performance and is, most notably, the underlying coupling software used in National
Center for Atmospheric Research Community Earth System Model (NCAR CESM).

OASIS3-MCT is a portable set of Fortran 77, Fortran 90 and C routines. Lowintrusiveness, portability and flexibility are OASIS3-MCT key design concepts. Aftercompilation OASIS3-MCT is a coupling library to be linked to the component models,
and which main function is to interpolate and exchange the coupling fields between
them to form a coupled system. OASIS3-MCT supports coupling of 2D logicallyrectangular fields but 3D fields and 1D fields expressed on unstructured grids are also
supported using a one dimension degeneration of the structures. Thanks to MCT, alltransformations, including regridding, are performed in parallel on the set of sourceor target component processes and all coupling echanges are now executed in

Deliverable D3.1

11 of 32

12/01/2017

Copernicus Climate Change Service

parallel directly between the component processes via Message Passing Interface(MPI). OASIS3-MCT also supports file I/O using netcdf.

The new version, OASIS3-MCT_3.0 supports coupling exchanges between
components deployed in much more diverse configurations than before. It is ofcourse possible to implement coupling exchanges between two components
corresponding to two different executables running concurrently on separate sets oftasks, as before, but also between two components running concurrently on separate
sets of tasks within one same executable, or between different sub-components
defined on separate or overlapping sets of tasks within one executable. It is also now
possible to have some or all tasks of a component not participating to the couplingexchanges.

In spite of the significant changes in underlying implementation, usage of OASIS3MCT in the codes has largely remained unchanged with respect to previous OASIS3
versions. To communicate with another component, or to perform I/O actions, acomponent model needs to include few specific calls of the Application Programmig
Interface (API) OASIS3-MCT coupling library. The namcouple configuration file is also
largely unchanged relative to OASIS3, although several options are either deprecated,
not used or not supported.

A more detailed presentation of Oasis is available here:
https://verc.enes.org/oasis/oasis3-mct_3-0-official-release

2.6 Eclis

The Eclis (Environment for CLImate Simulations) environment is used to define andrun the seasonal forecast experiments. The different model components (coupler
included) each have a namelist, almost all have a restart file, and most have their own
output format. Several binaries related to each model group are used. This

Deliverable D3.1

12 of 32

12/01/2017

Copernicus Climate Change Service

information is stored in a so-called param file used by Eclis. The binaries are launched
together using a MPI message passing software. A CNRM-CM run is made of an
automated sequence of "macro-jobs", where each "macro-job" iterates on a numberof months.

In this system, the ensemble members are generated by small differences in theirinitial conditions but also during the integration, in order to take into accountuncertainties in the initial state as well as model errors.

Deliverable D3.1

13 of 32

12/01/2017

Copernicus Climate Change Service

3. Generation of stochastic perturbations
3.1 Introduction

The role of stochastic perturbations in the forecast system is threefold:
1. to generate equi-probable ensemble members
2. to attempt to partly correct the model imperfections (in particular the systematic
error)
3. to address spurious model simulation failures/crashes
In previous forecast systems item 1 was managed by introducing a perturbation in an
initial atmospheric situation, or by lagging the initial situation by a few days. Item 2
was taken into account by considering forecast anomalies with respect to a modelclimatology (this is sill the case in the new system). Item 3 was managed by slightly
modifying the horizontal diffusion coefficient temporarily.

Three ingredients are necessary to activate the stochastic perturbation system in thehindcast as well as in the forecast production:
1. the perturbation fields
2. the random calendars
3. the definition of the ensembles
These 3 ingredients are then used in the model script.
3.2 The nudged pre-hindcasts: generating the perturbation fields

This is a hard and complex task. Each time the model or the ingredients (e.g. theinitial situations) are changed, the perturbation fields need to be re-created. Thismodel dependency is similar to the dependency between forecasts and hindcasts. It
is, however, not so crucial: a slight change like a change in computer should not makeregeneration necessary.
The fundamental idea is that a model is not perfect, and its equations contain errors:
dX/dt=true_equations(X,t)+model_errors(X,t)

Deliverable D3.1

14 of 32

12/01/2017

Copernicus Climate Change Service

where X is the vector describing the prognostic variables (temperature, moisture,
wind and surface pressure) and t is time. We have coded model_equations(X,t) and
we ignore true_equations(X,t) or, equivalently, model_errors(X,t).
If we can estimate model_errors(X,t) for a set of hindcasts, we cannot fully correct a
forecast, but we can re-inject the past errors at random to take into account theuncertainty about our model_equations(X,t) .
A way to estimate this error is to carry out a set of preliminary hindcasts during
which the model is linearly relaxed (or nudged) toward the true trajectory, here ERAinterim reanalysis (ERAI in the following). There is a big dilemma:
if the relaxation is strong, the model will follow ERAI closely, but the errors will beerrors of a short-range forecast (model starting from an analysis)
if the relaxation is weak, the model will be in a similar state as in a standard seasonal
forecast, but the relaxation terms will correspond to a small fraction of the error.
The method depends on the frequency of the random drawings and the intensity ofthe perturbation, which is linked to the relaxation time. Because weather regimes
have a characteristic time of circa 5 days, a natural value for the length between two
drawings is 5 days. We tested 6 hours, 5 days and 1 month with a previous low
resolution version of the model, and did not observed a systematic impact on scores.
The choice of the relaxation time is less obvious. In previous studies, we have triedvalues between 1 and 100 days. Values below 6 h are not realistic because ERAI is notavailable at a higher frequency. Using a 6 h relaxation time for both divergence andvorticity produces dynamical imbalances in the model, because the normal modes of
the Arpege and ECMWF models are different. Values above 100 days are useless, as
we want to account for model error during the first months of the forecasts. The
results show that a value of 10 days is a reasonable compromise for the scores of thedifferent variables in the different regions. See Batté and Déqué (2016) for moredetails on this technique.
The nudged experiment is based on 12*23 7-month simulations. There are indeed 12
calendar months and 23 available hindcast years (1993-2015). Note that a start dateon 01 December 2015 implies that the model is nudged toward ERAI till June 2016.
Deliverable D3.1

15 of 32

12/01/2017

Copernicus Climate Change Service

The nudged simulations are as close as possible to members 001 of the hindcasts,
with four differences
1. the model does not include stochastic perturbations
2. the model is nudged toward 6-hourly ERAI interim prognostic variables
3. the postprocessing is highly simplified
4. each day, the differences ERAI minus model variables, times the relaxation
factor, are saved in a file.
The script:~deque/mitraillette.shlaunches 23 simulations for a given starting month. In fact this script simply updates
another script~deque/traite_nudging_359l91.c3s
which prepares the environment for the model runs. The model script is~deque/arpege/mccV6.2.5.tl359l91r_nud_c3s
The daily correction files are FA files (special Arpege format based on grib) named
CR$YYYY$MM$DD and saved on/scratch/work/deque/corrections/corr_N$S
where $S is a letter indicating the starting month (A=January, B=February, …
L=December).
There are 4*91+1 records in each file (4 variables in 91 vertical levels + mean sea levelpressure). The fields are in spectral coefficients (T359 truncation).
Note that because the surface schemes are based on different empirical approaches
in Arpege and in ERAI, the relaxation is progressively dialled down to zero in thelowest 5 vertical levels (the lowest level being not relaxed at all). A symmetricaltreatment is applied the highest 5 levels (see namelist NAMNUD which controls
nudging in an Arpege run). This reduces the inconsistency at the ocean surface,
between a free ocean model and a driven atmosphere.

Deliverable D3.1

16 of 32

12/01/2017

Copernicus Climate Change Service

3.3 Random calendar

The random choice of a perturbation is done by selecting an arbitrary day and year
(not calendar month). This means that the random choice of the perturbations for agiven month during a forecast can be described by a random calendar, because eachday we need a new perturbation. Random calendars are created once for all and are
valid whatever a hindcast or a forecast, whatever the month or the year. They will bechanged only when the hindcast period is modified (not just extended by a couple ofyears), or when the method of taking a new random sequence every 5th day ismodified.
A random calendar is a monthly calendar: it allows to associate to each day of themonth a random day with the constraint that:the same calendar month is used
the year is taken at random amongst the years of the nudging phase (1993-2015); inthe hindcast phase this introduces a small (not significant) positive bias in the scoresby the fact that 4% of the perturbations use observed data from the month to bepredicted.
the days are consecutive during a 5-day sequence
A random calendar is a text file of 36 lines in the form:ln -sf $CORR/CR1994${MT}08 RX$YYYY${MM}0100…
ln -sf $CORR/CR1999${MT}08 RX$YYYY${MM}3500
ln -sf $CORR/CR1999${MT}05 RX$YYYN${MN}0100
The year and the first day in each line come from a random generation process. The
second day in each line corresponds to a sequence from 01 to 35. The last line
corresponds to the first day of the next month. It is necessary for the linear timeinterpolation at each time step in the model.
The reason for having 35 days whatever the month is to facilitate the generation ofpentads. Of course, only the actual days are used by the model (e.g. 31 in January, 28
ou 29 in February …). The 5-day (pentad) sequences are defined by the first day ofDeliverable D3.1

17 of 32

12/01/2017

Copernicus Climate Change Service

each sequence. This number is selected at random between the 01 and 24 of themonth. This allows to have a universal random calendar whatever the month. As aside effect the perturbations of day 29, 30 and 31 are never used by the model.
Because 23 years are available, there are (24*23)/7=78 possible calendars having nodate in common: one has to choose 7 pentad start dates (because 7*5=35) amongst
24*23 candidate days. This ensures that two ensemble members can never beidentical.
The 78 calendars are generated by the script:~deque/fic_cal_rand_c3s_1.shand gathered in three files:~deque/rand_calend28_n78_$NUM.tar
where NUM is a, b or c. Indeed, one can generate much more than 78 calendars, butthey might associate the same random date to a given date. The model uses series a.
When it crashes because of a numerical (reproducible) instability, it uses series bduring that month, then goes back to series a. Just in case, a third series of calendars
is available for a third attempt. This method of “butterfly effect” can fail if thenumerical explosion occurs during the first day of the month. In this case, one has tomanually go one month backwards and to replace series “a” by series “b”. The series
number is automatically reset to “a” at the end of a successful month.
The case of the first partial month (members others than 001) needs a specific care.
As there is a single member for the nudged simulations (see section 2 above), one has
to use for the runs starting at 20 or 25 of the month, the perturbation of the monthafter. However the amplitude of the perturbations (which are model errors)significantly increases with the range at the beginning of an initialized run. So, during
the first phase of the forecast, the start of the pentad is limited to day 08 of themonth. As a result, there are only (8*23)/7=26 possible non-overlapping calendars.
This is not a limitation, as there are 12 hindcasts or 25 forecasts starting at the samedate. The 26 calendars are in:~deque/rand_calend12_n26_$NUM.tar

Deliverable D3.1

18 of 32

12/01/2017

Copernicus Climate Change Service

which has been generated (once for all) by the same script as the standard random
calendars.
3.4 Definition of the ensembles

Once the perturbations and the calendar exist, one has to decide which calendar isused for which ensemble member. To simplify the process the choice will be thesame whatever the starting date. One could have made a further simplification byusing the same association between a member and its random calendar whatever themonth, but there is a drawback. In a random calendar, only 7 (and possibly less) years
are involved. If during each month a given member uses these same years, then theperturbations do not fully reflect the statistical distribution of model errors. For thisreason, a given member will use each month a different random calendar. There are
8 lists: one for month 0 (the end of the month in lagged members 002, 003 …), month
1, … month 7. They are gathered in file~deque/rand_liste.tar
which has been created by~deque/fic_cal_rand_c3s_2.sh
Each list contains 51 numbers (25 numbers for month 0) which associate an ensemble
member to the number of a random calendar (1 to 26 for month 0, 1 to 78 for month
1 to 7). In the case of a hindcast, only the first 25 (or the first 12 at month 0) numbers
are used.
3.5 Model implementation

With the above described ingredients, the implementation in the model script, used
for the hindcasts as well as for the forecasts is straightforward:
The file $HOME/choix_$EXPID gives the letter a, b or c of the series of random
calendars (NUMR). At the beginning NUMR=a and NUMR is reset to “a” at the end ofa successful run. If the run fails, NUMR is set to “b”, then to “c” by the script:~deque/repare4.sh

Deliverable D3.1

19 of 32

12/01/2017

Copernicus Climate Change Service

automatically submitted when the model crashes. Note that in case of timeout whenthe model is frozen by the system, this procedure is inefficient (and useless). The
security is ensured by the creation-destruction by the model script of a file:$HOME/encours_$EXPID
which is checked by a half-hourly cron. If the above file exists and the EXPID job is notin the queue, EXPID is resubmitted. This type of failure occurs much more often than
the numerical explosions (at Météo-France as well as at ECMWF). But the security
mentioned here has nothing to do with the stochastic perturbations.
The variable CORR which appeared in section 3 above indicates where to find theperturbation files. This is a huge directory (about 1 TB for one calendar month). It
may be saved on hendrix, but must be loaded on prolix before a forecast or ahindcast starts.
Then the model script needs to decide which type of random calendar to use. If thestart day (variable JJ) is not 01, one must use rand_calend12_n26_$NUMR.tar(shorter list), otherwise one uses rand_calend28_n78_$NUMR.tar , which is thestandard case. One needs to know if the number of the month (STEP=0 for lagging,
STEP=1 for first month, … STEP=7 for last month) is IPASS (for member 001) or IPASS1 (for other members). IPASS is the counter of the automatic launching procedure.
Variables MM, MN and MT are current month, next month and true monthrespectively. They are used in for UNIX variable substitutions in the random calendar
file. In general MT=MM, but for STEP=0, MT is the month after. For instance, if themodel starts on 25 November, then MM=11 and MT=12 because December
perturbations are used. Month MN is associated to year YYYN because of the possible
change of year.
Thanks to the list of calendars associated with the right step, one assigns to member$MEMBER a calendar number $NN. This calendar is extracted from the right tararchive and the file lnRANDOM$NN is executed. This file creates symbolic links
between existing perturbations (with random dates) and daily files which will be readeach day by the atmospheric model during the run. Note that links such as
RX1993022900 or RX1994013500 will never be used.

Deliverable D3.1

20 of 32

12/01/2017

Copernicus Climate Change Service

It is of highest importance that the namelist switch LDYSTO is set to .TRUE. . In the
Arpege namelist. With the default value (.FALSE.) the perturbations have the wrong
sign. In the nudging runs this switch must be at its default value. If not, the model
crashes rapidly, which is less detrimental than consuming hours of computer for adegraded result.

Deliverable D3.1

21 of 32

12/01/2017

Copernicus Climate Change Service

4. Generation of initial conditions
A forecast ensemble is based on 51 individual model integrations. These 51 membersof the forecast come from a combination of 3 batches of model integrations (akaruns) are launched. The batches differ by the initial dates which are 20th and 25th ofthe previous month and the 1st of the current month. Within a given batch, theforecast members differ by the application of random perturbations during the
integration following the stochastic dynamics technique (see section 3), thus
generating the ensemble spread. This is illustrated in Figure 2.
For each calendar month, a series of hindcasts is produced at least two months
before the forecast. It accounts for 25 members and spans 23 years from 1993 until
2015. The only differences with the forecast are the size of the ensembles and the
preparation of the initial states, which is managed in advance because the reanalyses
are already available. In order to maintain the best homogeneity between theforecasts and the hindcasts, 12 members start from the 20th of the previous month,
12 more members start from the 25th of the previous month, and the last member
starts from the 1st of the current month. Thus the diagram is very similar to Figure 2(replacing 25 members with 12 members). As opposed to the 51 forecasts which arelaunched in three batches (according to the earliest availability of the initial states)
the 25 hindcasts are launched in a single batch with 3 initial dates for a given initialmonth and year.

Deliverable D3.1

22 of 32

12/01/2017

Copernicus Climate Change Service

Figure 2 : Generation of the 51-members-ensemble of the seasonal forecast

4.1 Fetching the initial conditions (hindcast)

The initial conditions for Arpege come from ERA-interim reanalyses, extracted at 6hourly frequency from 1 January 1979 to the latest available date. The files are
extracted from MARS archived and converted into Arpege format by
cca:/home/ms/fr/tou/arpege/grib_to_fa_auto
The files, on the original grid tl255l60R are tarred by 10-day periods and sent to
Météo-France by the ectrans utility
ecgate:/home/ms/fr/tou/transfer_ec_beaufix_multi
Then the files are converted to the model geometry tl359l91r and tarred by months
byDeliverable D3.1

23 of 32

12/01/2017

Copernicus Climate Change Service

beaufix:/home/gmgec/mrga/deque/aladin/e927/arp2arp.V6.0.ER
AI_auto
Before launching a hindcast experiment for a given month, it is necessary to extract
the three starting situations, and also to prepare the Oasis initial states. This is doneby:prolix:/home/gmgec/mrga/deque/prep_previ_359l91.c3s
Most scripts like this are present both on prolix and beaufix.
The Nemo and Gelato reanalysis has been produced by MERCATOR-OCEAN (Clotile
Dubois, 2016, personnal communication). They are saved on hendrix on a daily basis.
prolix:/home/gmgec/mrga/deque/restartmercator
Allows to put the needed restarts at the right place on prolix. Note that the Nemo
restarts are necessary to create the Oasis restarts.
The Surfex initial conditions are derived from ERA-Interim surface fields.
The required fields are the land-sea mask, the orography, the temperature and soil
water content of the 4 soil layers available in Era-Interim, the albedo, density andtemperature of the snow cover, the low and high vegetation cover as well as thetypes of high and low vegetation.
These fields are retrieved and gathered into grib2 data files through the MARS systemon the Ecgate computing facility. These grib files are then uploaded on Météo-France
prolix supercomputer.
Finally, these fields are interpolated horizontally and vertically onto the Surfex gridwith a dedicated job, requiring a Surfex Physiographic data file, an adequate namelist
an executable binary file called PREP, and the Ecoclimap database that includes anecosystem classification and a coherent set of land surface parameters.
More details on this method can be found in Boisserie et al. (2015).

Deliverable D3.1

24 of 32

12/01/2017

Copernicus Climate Change Service

4.2 Fetching the initial conditions (forecast)

The Surfex initial conditions for the forecast are derived from the 00z IFS operational
analysis of the days that match the 3 aforementioned start dates. They are processed
as soon as the IFS analyses are made available by ECMWF. The processing stage issimilar to the one described in the previous section. It should be noted though that
due to their very high horizontal resolution (O1280 in Nov. 2016), the IFS operational
analyses are retrieved through MARS on a T255 coarser grid.

Every Wednesday our partner MERCATOR-OCEAN uploads a set of 14 files on the
Météo-France storage server hendrix in the following directory :hendrix:/home/s/spsy/spsy002/PSY2G3R4/db
These files describe 14 successive daily ocean states. Two of them are actual ocean
analyses resulting from the most recent data assimilation cycle while the other 12 areguesses issued from the ocean model integration.

Figure 2 shows an example of how the 51 members of the forecast are generated.
The start dates of month M forecasts, as stated above, correspond to :• The 20th and the 25th of month M-1• The 1st of month M
At these start dates, the ECMWF atmosphere analysis, and the most recent ocean
analysis are fetched and processed. On the first start date, 25 integrations of themodel begin, each one with a distinct set of stochastic dynamics perturbations. On
the second one, 25 more integrations are initiated similarly. The last integration
begins on the 1st day of the month.

Deliverable D3.1

25 of 32

12/01/2017

Copernicus Climate Change Service

5. Environment for CLImate Simulation (Eclis)
Eclis (Environment for CLImate Simulation) is the set of scripts and tools necessary to
run CNRM-CM. It does not include tools for preparing initial nor boundary conditions.
Basically, the script ’install’ allows to prepare an experiment, and defines anotherscript which will actually run the experiment.
Eclis manages an environment to run CNRM-CM simulations on supercomputers likebeaufix and prolix. It also manages the storage of the results.

5.1 Eclis organization

An Eclis version directory includes a version of tools ’relan’ and ’mtool’ which areconsistent with the need of the main scripts; it also contains the following directories:• param which provides useful examples of experiment parameter files; for details
on parameters see section 5.4 (Design a ’param_’ file)• testing, which sub-directory prolix includes parameter files for a very wide variety
of experiment configurations; they are provided as a reference of the possible
technical settings, but not as scientifically meaningful settings• scripts; where there is usually one single, non-numbered, version of theexperiment installation script and of each script needed for running a coupledexperiment ; when a bug is fixed for a script, this appears in the git history of therelease directory• plugins, which includes the code of the available plugins
The file cm_setup sets up the environment used by Eclis. It should be sourced fromthe user .profile. It is automatically launched by the scripts when needed.
5.2 Use of Eclis

Eclis consists in designing an experiment parameter file that ends with source$INSTALLER $* and to execute this file with arguments :-noask for avoiding being asked if a previous install should be erased-go for launching the experiment at the end of the (successful) install phase. This is
a way to create and launch an experiment at the end of a first one, when there is a
Deliverable D3.1

26 of 32

12/01/2017

Copernicus Climate Change Service

logical dependency (e.g. the first experiment creates the restarts that are necessary
for installing the 2nd one).

5.3 Design a ’param_’ file

Defining a CNRM-CM experiment is as simple as designing a so-called ’param_’ file,which filename must be of the form ’param_EXPID’, where EXPID is the name given to
your experiment. This file, in bash syntax, sets values for Eclis parameters and thensources the Eclis install script.

5.4 Standard plugins

Plugins are sets of shell-script commands that Eclis can take into account during
CNRM-CM experiment runs, in order to perform actions atop of the strict running of
the coupled or atmosphere-only or ocean-only scripts. Special plugins have beendeveloped for the specificities of seasonal forecast.

Deliverable D3.1

27 of 32

12/01/2017

Copernicus Climate Change Service

6 The basic set of scripts
As stated in part 1, the seasonal ensemble forecast is launched 3 times per month. At
each start date (the 20th, the 25th and the 1st ), a cron job-scheduler executes the
script lance_forsys6.sh.
This first script generates one “param_F????????” file for each member of theensemble forecast. It contains the initial date and the end date of the forecast. It also
sources the param_generique file which is the main file to launch a forecast with Eclis(see section 4). At the end of lance_forsys6.sh, the created script param_F???????? isexecuted. So, it sets values for Eclis parameters and then sources the Eclis install
script.

Another cron scheduled script is executed every 3 hours to monitor the running jobs.
It is installed on the 'sxpastel' CNRM computing facility but launches the script
prolix:/home/gmgec/mrga/ardilouzec/monitrel.sh
via ssh.
This monitoring consists in browsing the fileprolix:/scratch/work/ardilouzec/output/en_cours.txt
which contains the IDs of the ongoing jobs. A job ID is automatically written into thisfile when the job is launched, and deleted from this file as soon as they successfully
end. The script monitrel.sh checks that every job ID in the file is also present in the
supercomputer Slurm scheduling queue. If not, it automatically relaunches the job. It
gives up after two trials per job.

Deliverable D3.1

28 of 32

12/01/2017

Copernicus Climate Change Service

7. Post processing
The different components of the model (atmosphere, ocean, surface) generate
output files to be post-processed. For the atmosphere and surface, the fields areinterpolated on a 1°x1° grid with 181 latitudes and 360 longitudes starting at
90°S,0°W and extending northwards and eastwards. For the ocean, the files areprovisionally on the Nemo grid. The data (one file per field and per month) are first
stored on
prolix:/scratch/work/ardilouzec/output/series_nc
The format is netcdf (with packing for the surface and atmosphere and with zip
compression for the ocean). The files are transferred by the ectrans utility to ECMWF
on:
cca:/sc1/tcwork/zac/lb/copernicus/
For each starting month and member, a directory is created with a name containing
the date and the member number. For example directory 199303_001 contains thefiles created for member 1 of the March 1993 hindcasts. Note that the start dates arein this case 20 Feb, 25 Feb ans 01 Mar. There are one netcdf file per month and per
variable in each directory (i.e. 329 files). The file transfer is managed by a script
runnig at ECMWF on computer cca (or ccb):
cca:/sc1/home/zac/transfer_prolix_ec3_auto
This script is automatically relaunched in a double loop, the innermost running on theensemble members (forecasts and hindcasts), the outermost running on the years(hindcasts only). The file monotoring this double loop is:
cca:/sc1/home/zac/relances/AA3/AA3_his
The automatic launching system is the same as used by Eclis (see section 5).

Deliverable D3.1

29 of 32

12/01/2017

Copernicus Climate Change Service

Table 2 lists the daily variables. In addition, land-sea mask and geopotential of surface
orography are available.
Table 2: list of daily variables archived

Every 6 hours
2 metre temperature
2 metre dewpointtemperature
10 metre u wind
10 metre v windmean sea level pressure
total cloud coverskin temperature
Every 12 hours
geopotential
temperature
specific humidity
u/v wind components
At the following 11
pressure levels:
1000, 925, 850, 700, 500,
400, 300, 200 hPa

Every 24 hours
Every 24 hours,
sea surface temperatureaccumulated
total precipitation
total soil moisturesnow fallsurface temperaturesnow depth (water
surface sensible heat flux
surface latent heat flux
equivalent)
Tmax at 2 m (over previous surface solar radiation
downwards
24 h)Tmin at 2 m (over previous surface thermal radiation
downwards
24 h)surface solar radiation
surface thermal radiation
top solar radiation
top thermal radiation
east-west surface stressnorth-south surface stressevaporation

For the ocean:Every 24 hours, mean:sea levelsea ice concentration
sea surface temperature
depth of 28 deg isotherm
depth of 26 deg isotherm
depth of 20 deg isotherm
mixed layer depth
surface salinity
heat content over the first 300 m
zonal surface current
meridional surface current

Deliverable D3.1

30 of 32

12/01/2017

Copernicus Climate Change Service

8 References
Batté, L., and Déqué, M. (2016). Randomly correcting model errors in the ARPEGE-Climate v6.1 component of
CNRM-CM : applications for seasonal forecasts. Geosci. Model Dev. 9 : 2055-2016, doi : 10.5194/gmd-9-20552016.
Boisserie, M., Decharme, B., Descamps, L., & Arbogast, P. (2016). Land surface initialization strategy for a
global reforecast dataset. Quarterly Journal of the Royal Meteorological Society.
Courtier P., J.F. Geleyn (1988). A global numerical weather prediction model with variable resolution:
Application to the shallow water equations. Quart. J. Roy. Meteor. Soc. 114, 1321–1346.
Cuxart, J., Bougeault, Ph. and Redelsperger, J-L., 2000 : A turbulence scheme allowing for mesoscale andlarge-eddy simulations. Quart. J. R. Met. Soc., 126, 1-30.
Decharme, B., Alkama, R., Papa, F., Faroux, S., Douville, H., & Prigent, C. (2012). Global off-line evaluation of
the ISBA-TRIP flood model. Climate dynamics, 38(7-8), 1389-1412.
Decharme, B., Brun, E., Boone, A., Delire, C., Le Moigne, P., & Morin, S. (2015). Impacts of snow and organicsoils parameterization on North-Eurasian soil temperature profiles simulated by the ISBA land surface model.
The Cryosphere Discussions, 9, 6733-6790.
Decharme, B., Martin, E., & Faroux, S. (2013). Reconciling soil thermal and hydrological lower boundary
conditions in land surface models. Journal of Geophysical Research: Atmospheres, 118(14), 7819-7834.
Déqué M., C. Dreveton, A. Braun, D. Cariolle (1994). The ARPEGE/IFS atmosphere model: a contribution to the
French community climate modelling.Climate Dynamics 10, 249-266.
Guérémy, J. F., 2011 : A continuous buoyancy based convection scheme: one and three dimensional
validation. Tellus A, 63, 687-706.
Le Moigne, P., Colin, J., & Decharme, B. (2016). Impact of lake surface temperatures simulated by the FLake
scheme in the CNRM-CM5 climate model. Tellus A, 68.
Lopez, Ph., 2002 : Implementation and validation of a new prognostic large-scale cloud and precipitation
scheme for climate and data-assimilation purposes. Quart. J. R. Met. Soc., 128, 229-257.
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A.,
Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K.,
Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S.,
Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G.,
Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A. (2013). The
SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables andfluxes, Geosci. Model Dev., 6, 929-960, doi:10.5194/gmd-6-929-2013.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer forinhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of GeophysicalResearch: Atmospheres (1984–2012), 102(D14), 16663-16682.

Deliverable D3.1

31 of 32

12/01/2017

Copernicus Climate Change Service

Morcrette, J. J. (1990). Impact of changes to the radiation transfer parameterizations plus cloud optical.
Properties in the ECMWF model. Monthly Weather Review, 118(4), 847-873.
Piriou, J. M., Redelsperger, J. L., Geleyn, J. F., Lafore, J. P., and Guichard, F., 2007 : An approach for convective
parameterization with memory : Separating microphysics and transport in grid-scale equations. Journal of the
Atmospheric Sciences, 64, 4127-4139
Salas y Mélia D (2002) A global coupled sea ice-ocean model. Ocean Model 4:137–172
Sommeria, G. and Deardorff, J.W., 1977 : Subgrid-scale condensation in models of nonprecipitating clouds. J.
Atmos. Sci., 34, 344-355.
Vergnes, J. P., Decharme, B., & Habets, F. (2014). Introduction of groundwater capillary rises using subgrid
spatial variability of topography into the ISBA land surface model. Journal of Geophysical Research:
Atmospheres, 119(19).

Deliverable D3.1

32 of 32

12/01/2017

Copernicus Climate Change Service

ECMWF, Shinfield Park, Reading RG2 9AX,
UK
Contact: Samuel.almond@ecmwf.int

climate.copernicus.eu

copernicus.eu

ecmwf.int