CMCC SPS3 Operational Seasonal Prediction System Synthetic System Description

Table of Contents

  1. Table of Contents
  2. Introduction
  3. The CMCC SPS3 Operational Seasonal Prediction System
    1. Description of the Operational Prediction System
      1. Atmosphere
      2. Ocean
      3. Sea Ice
      4. Land Surface
      5. River Routing
      6. The Coupler
    2. Initial Conditions and Initial Condition Perturbations
      1. Atmospheric Initial Conditions and Perturbations
      2. Land Initial Condition and Perturbations
      3. Ocean Initial Condition and Perturbations
      4. Sea-Ice Initial Condition and Perturbations
      5. Combination and Choice of Perturbed Initial Conditions to generate the Ensemble
    3. Atmosphere, Ocean and Post-Processing Grids
      1. The horizontal and vertical grids of CAM5.3
      2. The horizontal and vertical grids of NEMO
      3. Post-processing output grid and re-gridding methods
      4. Forecast and Hindcast Ensemble Size and available initial dates
    4. CMCC-SPS3 Table of System Characteristics
    5. Provision of Global Forecasts Fields
    6. Provision of Verification Statistics
  4. Dissemination
  5. CMCC-SPS3 participation in Multi-Model Ensembles
  6. Additional Information
  7. Future Developments
  8. References


This document has the purpose of describing the CMCC SPS3, i.e. the Operational, Ensemble-based, Seasonal Prediction System operated at CMCC (Euro-Mediterranean Center on Climate Change). This Seasonal Prediction System has been developed at CMCC on the basis of the models described below and routinely performs forecasts on the seasonal time scale which are provided, among others, to Copernicus-C3S and to WMO LC-LRFMME (WMO Lead Centre for Long-Range Forecast Multi-Model Ensemble). A selection of products and verifications are also available via a dedicated CMCC public website

The CMCC SPS3 Operational Seasonal Prediction System

The acronym and full name of the System is CMCC-SPS3, i.e. Euro-Mediterranean Center for Climate Change - Seasonal Prediction System, Version 3. The System is based on a comprehensive coupled Ocean-Atmosphere Global Climate Model, complemented by a number of additional modules.

Description of the Operational Prediction System

The current version of the CMCC-SPS3 System consists of several independent but fully coupled model components simultaneously simulating the Earth’s atmosphere, ocean, land, sea ice and river routing, together with a central coupler/driver component that controls data synchronization and exchange (see the sketch of Figure 1).

The CMCC-SPS3 atmospheric, land surface, sea ice and river routing model components are based on CESM, the NCAR Community Earth System Model version 1.2.2 (in their CAM5.3, CLM4.5, CICE4 and RTM versions, respectively). A detailed description of such models is given in Hurrell et al. (2013) and references therein. The ocean component is based on NEMO, the European Nucleus for European Modelling of the Ocean model, in its 3.4 version; for a detailed description, see Madec et al. (2008). For a first evaluation of CMCC-SPS3 performance (bias and skill), see Sanna et al. (2018).

Figure 1 – General scheme of the CMCC-SPS3 fully coupled Seasonal Prediction System


The atmospheric component of CMCC-SPS3 is the Community Atmosphere Model version 5 (CAM5.3, see Neale et al., 2010 for a description of the model macrophysics) which can be configured to use a spectral transform, a finite volume or a finite elements dynamical core. The atmosphere implemented in CMCC-SPS3 is hydrostatic and uses the spectral element configuration (a formulation of the spectral element method that uses high-degree hybrid polynomials as basis functions can be found in Patera, 1984), with a horizontal resolution of about 110 km, 46 vertical levels up to about 0.3 hPa and an integration time-step of 30 minutes.

A description of the treatment for stratiform cloud formation, condensation, and evaporation macrophysics is given in Neale et al. (2010). A two-moment microphysical parameterization (Morrison and Gettelman, 2008; Gettelman et al. 2008) is used to predict the mass and number of smaller cloud particles (liquid and ice), while the mass and number of larger-precipitating particles (rain and snow) are diagnosed. Cloud microphysics interacts with the model’s greenhouse gas concentration, where observed yearly values are specified before 2005 and CMIP5 protocol concentrations (scenario RCP8.5) are used after 2005, see IPCC (2013). Differently from the standard CAM5.3 version, in CMCC-SPS3 the aerosol distribution does not evolve in time through the modal aerosol model (MAM) but is taken from a fixed climatology (referring to year 2000). A Rapid Radiative Transfer Model for GCMs (RRTMG; Iacono et al., 2008, Bretherton et al., 2012, Liu et al., 2012) is used to calculate the radiative fluxes and heating rates for gaseous and condensed atmospheric species. A statistical technique is used to represent sub-grid-scale cloud overlap (Pincus et al., 2003). Moist turbulence (Bretherton and Park, 2009) and shallow convection parameterization schemes (Park and Bretherton, 2009) are used to simulate shallow clouds in the planetary boundary layer.

The process of deep convection is treated with a parameterization scheme developed by Zhang and McFarlane (1995) and modified with the addition of convective momentum transports by Richter and Rasch (2008) and a modified dilute plume calculation following Raymond and Blyth (1986, 1992). Moist convection occurs only when there is convective available potential energy (CAPE) for which parcel ascent from the sub-cloud layer acts to destroy the CAPE at an exponential rate using a specified adjustment time scale.

This version of CAM5 uses a modified vertical grid that with 46 vertical levels and a model top at 0.3 hPa and includes a parameterization of non-orographic gravity waves following Richter et al. (2014). The convective gravity wave efficiency is adjusted to produce a QBO period in the lower stratosphere closer to observations.


The Nucleus for European Modelling of the Ocean (NEMO) is the ocean model of CMCC-SPS3. The NEMO model solves the primitive equations subject to the Boussinesq, hydrostatic and incompressibility approximations. The prognostic variables are the three velocity components, the sea surface height, the potential temperature and the practical salinity.

The ocean component used in CMCC-SPS3 is based on the eddy-permitting Version 3.4 of NEMO, with a horizontal resolution of about 25 km, 50 vertical levels (31 in the first 500 m) and an integration time-step of 18 minutes.

In the horizontal, the model uses a nearly isotropic, curvilinear, tri-polar, orthogonal grid with an Arakawa C–type three-dimensional arrangement of variables. The model is integrated in its eddy-permitting, 1/4° resolution configuration. In the vertical, a partial step z-coordinate is used.

The model uses a filtered, linear, free-surface formulation, where lateral water, tracers and momentum fluxes are calculated using fixed-reference ocean surface height. The time integration scheme used is a Robert–Asselin filtered leapfrog for non-diffusive processes and a forward (backward) scheme for horizontal (vertical) diffusive processes (Griffies, 2004). The linear free-surface is integrated in time implicitly using the same time step.

NEMO uses a non-linear equation of state. Tracers advection uses a Total Variance Dissipation (TVD) scheme while momentum advection is formulated in vector invariant form, using an energy and enstrophy conserving scheme (Zalesak, 1979). The vertical turbulent transport is parameterized using a Turbulent Kinetic Energy (TKE) closure scheme (Gaspar et al., 1990) plus parameterizations of double diffusion, Langmuir cell and surface wave breaking. An enhanced vertical diffusion parameterization is used in regions where the stratification becomes unstable. Tracers’ lateral diffusion uses a diffusivity coefficient scaled according to the grid spacing, while lateral viscosity makes use of a space-varying coefficient. Both are parameterized by a horizontal bi-Laplacian operator. Free-slip boundary conditions are applied at the ocean lateral boundaries. At the ocean floor, a bottom intensified tidally-driven mixing (Simmons et al., 2004), a diffusive bottom boundary layer scheme and a nonlinear bottom friction are applied. No geothermal heat flux is applied through the ocean floor. The shortwave radiation from the atmosphere is absorbed in the surface layers using RGB chlorophyll-dependent attenuation coefficients. No wave model is included.

Sea Ice

The sea ice component is version 4 of the Community Ice CodE (CICE4, Hunke et al., 2010) which uses the same horizontal grid of the ocean model, but an integration timestep of 30 minutes. It includes the thermodynamics of Bitz and Lipscomb (1999), the elastic–viscous–plastic dynamics of Hunke and Dukowicz (1997). It also contains a multiple scattering shortwave radiation treatment (Briegleb and Light, 2007, Holland et al., 2012) and associated capabilities to simulate explicitly melt pond evolution and the deposition and cycling of aerosols (dust and black carbon) within the ice pack.

Land Surface

The land component of the CMCC-SPS3 forecast system is the Community Land Model (CLM4.5, Oleson et al., 2013). CLM4.5 runs at the same resolution as the atmospheric model (about 1°), with a 30-minute time-step. The configuration incorporated in CMCC-SPS3 (the so-called Satellite Phenology version of CLM4.5) uses only a simplified vegetation dynamics which includes a treatment of mass and energy fluxes associated with prescribed temporal (seasonal) change in land cover due to LAI (Leaf Area Index) but not to Plant Functional Types (PFTs), which are kept constant in time during the six-month integration. No evolving biosphere or crop model are therefore present and plant phenology (LAI) is determined through a seasonally-dependent satellite climatology.

The snow model incorporates the Snow, land-Ice and Aerosol Radiation (SNICAR) model (Flanner et al. 2007). SNICAR includes aerosol deposition of black carbon and dust, grain-size dependent snow aging, and vertically resolved snowpack heating. A perched water table above icy permafrost ground is also present (Swenson et al., 2012).

The lake model has a representation of surface water (Subin et al., 2012), permitting prognostic wetland distribution. The energy fluxes are calculated separately for snow/water-covered and snow/water-free land and glacier units.

River Routing

The RTM (River Transport Model) routes total runoff from the land surface model to either the active ocean, or to marginal seas with a design that enables the hydrologic cycle to be closed (Branstetter, 2001; Branstetter and Famiglietti, 1999). The horizontal resolution is half-degree (about 50km) and the integration time-step is three-hourly.

The Coupler

All system components are synchronized by the CESM coupler/driver (CPL7, Craig et al., 2011). The coupling architecture provides plug-and-play capability of data and active components. The coupling frequencies are:

Atmosphere-Ocean: 90 minutes (every third time-step of the atmospheric model).

Atmosphere-Land: 30 minutes (every time-step of the atmospheric model).

Atmosphere-Sea Ice:30 minutes (which is the same time-step of the atmospheric model).

Initial Conditions and Initial Condition Perturbations

Initial condition (IC) fields for all necessary forecast modules (atmosphere, ocean, land and ice) are prepared routinely to initialize the monthly operational forecast. Additionally, a variety of perturbation techniques are applied to such initial conditions in order to obtain additional IC fields necessary to produce Ensemble Forecasts.

Atmospheric Initial Conditions and Perturbations

Atmospheric Initial Conditions (ICs) are provided by ECMWF operational IFS 00UTC analyses for the first of the month as extracted from the MARS database on a regular lat-lon grid at ½° resolution. They are then interpolated onto the model quasi-regular cubed-sphere grid (see Sect. 2.5 here below).

9 further perturbed atmospheric initial conditions are obtained by applying a time-lagging technique, that is using previous ECMWF analyses at 12 hour steps up to 5 days before (e.g. atmospheric perturbations are equivalent to 0.5,1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5 and 5 day tendencies of all model prognostic field variables). Before forecast integration, 12UTC data are further integrated for 12 hours up to 00UTC. This procedure finally provides 10 alternative atmospheric initial conditions from 00UTC of the first day of the month.

In the case of hindcasts, ECMWF operational analyses are substituted by ERA-Interim analyses (Dee et al., 2011).

Land Initial Condition and Perturbations

Land initial conditions are obtained from a one-month run ending on forecast initial date, forced by an observed atmosphere. This forced run is, in turn, initialized from a fixed 20-year spin-up run. Soil moisture and snow fields are also initialized from the same one-month forced run.

In order to generate three alternative land initial conditions, perturbations are obtained by using in turn re-analyses from ECMWF, NCEP and the mean of the two as forcing observed atmosphere. This provides three land initial conditions. In the case of operational forecasts, the observed atmosphere is provided by ECMWF operational analyses or by NCEP re-analyses or by a mean of both. In the case of hindcasts, the observed atmosphere is provided by ECMWF ERA-Interim or by NCEP re-analyses (Kalnay et al., 1996), or by a mean of both. This yields three possible initial conditions for the land surface.

Ocean Initial Condition and Perturbations

Ocean Initial Conditions are obtained by a 3D-VAR intermittent ocean data assimilation cycle performed with C-GLORS. Perturbations of initial conditions are obtained by re-assimilating observed data after insertion of added perturbations on SLA and on In-Situ profile observations of temperature and salinity (Burgers et al., 1998) and by perturbing the atmospheric fluxes and the ocean model equation of state (EOS) for seawater, during the integration of the assimilating model (Brankart, 2013). No unperturbed control forecast is used for the ocean model.

Sea-Ice Initial Condition and Perturbations

In order to produce Sea-Ice Initial Conditions, observed data of sea-ice concentration (SIC) and sea-ice-thickness (SIT) are assimilated, using on-line nudging schemes with a relaxation time scale of 8 hours and 5 days respectively. The observed SIC field is downloaded from the National Snow and Ice Data Center (NSIDC) (Cavalieri et al., 1999), while the SIT is constrained towards PIOMAS data (Pan-Arctic Ice Ocean Modeling and Assimilation System, Zhang and Rothrock, 2003) available for the Arctic area.

The assimilation of SIC and SIT data is performed during the C-GLORS ocean data-assimilation system, where, however, the LIM2 ice model within the NEMO ocean model substitutes the CICE4 used during coupled forecast integration. The LIM2 sea ice is the Louvain-la-Neuve Sea Ice Model (Fichefet and Morales Maqueda, 1997) which includes the representation of both thermodynamic and dynamic processes. The ice dynamics are calculated according to external forcing generated from wind stress, ocean stress and sea-surface tilt and to internal ice stresses. Internal ice stresses are computed using the elastic viscous-plastic (EVP) formulation of ice dynamics by Hunke and Dukowicz (1997) on a C-grid (Bouillon, Maqueda, Legat, and Fichefet, 2009).

No ice data are perturbed during data assimilation, however slightly different sea-ice data can be produced by the ocean data multiple perturbation procedure which generates the 8 alternative ocean initial conditions (4 for hindcasts).

Combination and Choice of Perturbed Initial Conditions to generate the Ensemble

The 10 atmospheric perturbed ICs, the 3 land perturbed ICs and the 8 (4 in hindcast mode) ocean perturbed ICs are combined to yield 240 (120 in hindcast mode) possible perturbed ICs among which the 50 ICs (40 ICs in hindcast mode) to produce the forecast ensemble are chosen at random.

Atmosphere, Ocean and Post-Processing Grids

The horizontal and vertical grids of CAM5.3

The atmospheric model’s horizontal grid is the so-called Cubed-Sphere grid (see Figure 2) first used in Sadourny (1972). Each cube face is mapped to the surface of the sphere with the equal-angle gnomonic projection (Rancic et al., 1996). The vertical grid/coordinate is an eta-type coordinate, following Simmons and Burridge (1981). The horizontal resolution is about 110 km and the model has 46 vertical levels, up to about 0.3 hPa.

Figure 2: Tiling the surface of the sphere with quadrilaterals. An inscribed cube is projected to the surface of the sphere. The faces of the cubed sphere are further subdivided to form a quadrilateral grid of the desired resolution. Coordinate lines from the gnomonic equal-angle projection are shown, see e.g. Sadourny (1972).

The horizontal and vertical grids of NEMO

In this operational, global configuration, NEMO uses, in the horizontal, an ORCA-family, curvilinear, tripolar, orthogonal grid (based on Mercator projection), which has a pole in the Southern Hemisphere, collocated with the geographic South Pole, and two poles placed on land in the Northern Hemisphere (in Siberia and Canada), in order to overcome the Pole singularities.

Figure 3: ORCA mesh conception. The departure from an isotropic Mercator grid start poleward of 20. The two “north pole” are the foci of a series of embedded ellipses (blue curves) which are determined analytically and form the i-lines of the ORCA mesh (pseudo latitudes). Then, following Madec and Imbard (1996), the normal to the series of ellipses (red curves) is computed which provide the j-lines of the mesh (pseudo longitudes).

The ORCA grid is based on the semi-analytical method of Madec and Imbard (1996). It allows to construct a global orthogonal curvilinear ocean mesh, which has no singularity point inside the computational domain, since two north mesh poles are introduced, in addition to the South Pole, and placed on land. The method involves defining an analytical set of mesh parallels in the stereographic polar plan, computing the associated set of mesh meridians and projecting the resulting mesh onto the sphere. The set of mesh parallels used is a series of embedded ellipses which foci are the two mesh north poles (see Figure 3). The resulting mesh presents no loss of continuity in either the mesh lines or the scale factors, or even the scale factor derivatives over the whole ocean domain, as the mesh is not a composite mesh. Poleward of 20°N, the two NH poles introduce a weak anisotropy over the ocean areas.

In the vertical, a partial step z-coordinate is used.

The horizontal resolution of the tri-polar grid is approximately 25 km and the ocean model has 50 vertical levels (31 in the first 500 m).

Post-processing output grid and re-gridding methods

The final output data are gridded on a regular lat-lon grid of 1x1°. Three-dimensional variables are provided on Standard Pressure Levels in the vertical. Surface fields are provided on the model’s orography, which is also an output field.

Re-gridding from the models’ grids to the post-processing output grid are performed using MPI CDO command-line Operators (see Sect. 2.5 here below).

Forecast and Hindcast Ensemble Size and available initial dates

Forecast ensemble size 50 members

Hindcast ensemble size 40 members

Hindcast time coverage 1/1993-12/2016

Pre-Operational Forecasts time coverage 1/2017-3/2018

Operational Forecasts time coverage 4/2018-now

CMCC-SPS3 Table of System Characteristics

Date of implementation of the current seasonal forecast system 2015
Whether the system is a coupled ocean-atmosphere forecast system (Y/N) Yes (CAM5.3, NEMO3.4, CLM4.5, CICE4)
Atmospheric model and its resolution CAM5.3, 1° x 1° km approximately
Oceanic model and its resolution NEMO3.4, ¼° x ¼° approximately
Source of atmospheric initial conditions

ECMWF ERA-interim (for hindcasts)

ECMWF IFS (for forecasts)

Source of oceanic initial conditions C-GLORS Global Ocean Intermittent 3D-VAR
Hindcast period 1/1993-12/2016
Ensemble size for hindcasts 40
Method of configuring the forecast ensemble Progressive 12h time-lagged atmospheric analyses (10 perturbations, 5 days backward), 3 perturbed land analyses (different observed atmospheric forcing), 8 obs-data-perturbed ocean OI analyses, then random sample of 50 ensemble members among 240 possible combinations
Length of forecast 6 months
Data format Netcdf and GRIB1
The latest date that predicted anomalies for the next month/season become available 8th of each month
Method of construction of the forecast anomalies Departures from model climate estimated from hindcasts dataset
URL where forecasts are displayed (public, with registration)
Point of contact

Table 1: CMCC-SPS3 Seasonal Forecasting System Characteristics

Provision of Global Forecasts Fields

The System is operated monthly (start date is first of the month) in Ensemble seasonal mode (6-month predictions, 50 ensemble members) and is completed by a database of monthly ensemble hindcasts (40 ensemble members) covering the period 1993-2016. This hindcasts database can be used to evaluate the performance of the system and to apply bias removal techniques from operational forecasts. The first operational seasonal forecast run produced for Copernicus-C3S, and contained in Copernicus-CDS, was initiated from April 1st, 2018, 00:00 UTC and monthly, from the first day of every month, since then. All further monthly seasonal forecasts (all with a forecast horizon of six months) from January 1st, 2017 until March 1st, 2018 are also available on Copernicus C3S-CDS as POPs, Pre-Operational Predictions. This constitutes, to date, a continuous database of 26 complete years of monthly ensemble seasonal (6-month) forecasts from January 1st, 1993 to the present date (December 2018). Further to this, CMCC has in fact been operating routine ensemble seasonal forecasts also with previous versions of the CMCC-SPS system since the year 2014.

Figures 4 and 5 show an example of the global JFM 2m temperature and relative probability forecast (terciles) and the El-Nino 3.4 index predicted by the operational seasonal forecast issued on December 1st, 2018 (lead 1).

Figure 4. CMCC-SPS3 ensemble seasonal forecast from start date December 1st, 2018. 2m Temperature, JFM three-month mean. Top: Ensemble mean. Bottom: Probability forecast expressed in Terciles. Anomalies are computed from the model’s own climate as computed from the 24-year database of hindcasts (1993-2016).

Figure 5. Nino 3.4 index forecast from start date December 1st, 2018 based on Ensemble monthly means.

Provision of Verification Statistics

Before entering into operations and constantly thereafter, the CMCC-SPS3 has been and is constantly subjected to extensive verification procedures and skill scores computation. Such verification procedures include the computation and plot of a number of different metrics (e.g. Bias, RMS error, Anomaly Correlation Coefficient, ROC Score of, e.g., T2m and Precipitation on a global scale both for deterministic and probabilistic forecast. Figure 6 and Figure 7 show two examples of global fields of such metrics: Figure 6 shows the T2m Anomaly Correlation Coefficient (ACC) computed from all December 1st hindcasts, for lead season 1. Figure 7 shows ROC Score for T2m, lead season 1 relative to lower, middle and upper terciles (Panels 7a, 7b and 7c respectively).

Figure 6. Anomaly Correlation Coefficient (ACC) for 2m Temperature computed from all 24 years (1993-2016) December 1st hindcasts (40 members Ensembles), lead season 1.

Figure 7a. Relative Operating Characteristics (ROC) Score for 2m Temperature, lead season 1 relative to the lower tercile. ROC computed from all 24 years (1993-2016) December 1st hindcasts (40 members Ensembles).

Figure 7b. Relative Operating Characteristics (ROC) Score for 2m Temperature, lead season 1 relative to the middle tercile. ROC computed from all 24 years (1993-2016) December 1st hindcasts (40 members Ensembles).

Figure 7c. Relative Operating Characteristics (ROC) Score for 2m Temperature, lead season 1 relative to the upper tercile. ROC computed from all 24 years (1993-2016) December 1st hindcasts (40 members Ensembles).


Currently, CMCC Operational Real-Time Seasonal Forecasts and Verification Statistics are available on the public Copernicus Web-site for Seasonal Forecast products:

An alternative set of three-monthly mean products is currently also available on a public CMCC website at address Temperature and precipitation probability (in terciles) forecasts are also available in graphical form on the same public access website.

In addition, CMCC Operational Real-Time Seasonal Forecasts and Verification Statistics are available on the WMO LC-LRFMME (WMO Lead Centre for Long-Range Forecast Multi-Model Ensemble) website at:

CMCC Seasonal Forecasts are also disseminated via the Mediterranean Outlook Forum (MEDCOF) Consensus Forecast and the Asian-Pacific Climate Center (APCC) Multi-Model System.

CMCC-SPS3 participation in Multi-Model Ensembles

CMCC Operational Seasonal Forecast is part of the following Multi Model Ensembles:

  • Copernicus Operational Multi-Model Forecasting System (C3S-CDS)
  • WMO LC-LRFMME (WMO Lead Centre for Long-Range Forecast Multi-Model Ensemble)
  • MEDCOF Multi-Model Consensus Forecast
  • APCC Multi-Model Ensemble Forecasting System

Additional Information

More extensive information on the Operational Forecast System Models can be found in the websites listed below.

More detailed documentation on CAM Model at:

More detailed documentation on CLM Model at:

More detailed documentation on NEMO Model at:

More ocean DA details available at:

More DA details on ECMWF operational analysis documentation at:

More DA details on NCEP operational analysis and reanalyses documentation at:

More information on MPI CDO routines at:

Documentation on the system’s/models’ climatology and performance can be found at:

Future Developments

It is planned that, during 2019, CMCC will implement an upgrade of SPS3, which will consist of a possible atmospheric model version change and of an increase of the atmospheric model resolution, from about 1x1° to either ½x½° or ¼x¼°, approximately. All other operational characteristics and parameters will remain the same.


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