A fully coupled atmosphere–ocean–ice model has been used
to produce global weather forecasts at Environment and Climate Change Canada (ECCC) since November 2017.
Currently, the system relies on four uncoupled data assimilation (DA) components
for initializing the fully coupled global atmosphere–ocean–ice forecast model:
atmosphere, ocean, sea ice and sea surface temperature (SST).
The goal of the present study is to implement a weakly coupled data assimilation (WCDA)
between the atmosphere and ocean components and evaluate its performance against uncoupled DA.
The WCDA system uses coupled atmosphere–ocean–ice short-term forecasts
as background states for the atmospheric and the ocean DA components that independently compute atmospheric and ocean analyses.
This system leads to better agreement between the coupled atmosphere–ocean analyses
and the coupled atmosphere–ocean–ice forecasts than between the uncoupled analyses and the coupled forecasts.
The use of WCDA improves the atmospheric forecast score near the surface,
but a slight increase in the atmospheric temperature bias is observed.
A small positive impact from using the short-term SST forecast
on the satellite radiance observation-minus-forecast statistics is noted.
Ocean temperature and salinity forecasts are also improved near the surface.
The next steps toward stronger DA coupling are highlighted.
Until recently, separate systems for atmospheric and ocean prediction have been used
in the generation of operational forecast products
at Environment and Climate Change Canada (ECCC).
The numerical weather prediction (NWP) system used a prescribed sea surface temperature (SST), while
the ocean prediction system used the atmospheric forcing from the NWP system.
However, it is established that coupled models can produce improved forecasts
on various timescales .
At this time, several operational centers use coupled models to generate forecasts
(e.g. ECCC, ; Met Office, ; ECMWF, ).
At ECCC, the fully coupled atmosphere–ocean–ice model
has been used operationally to produce weather forecasts since November 2017.
Since coupled models have been shown to provide significant improvements versus uncoupled models for NWP systems,
research leading to the implementation of various coupled data assimilation (CDA) strategies
has started to potentially further improve the forecast skill.
A review of current activities on coupled prediction systems and CDA may be found in .
The World Meteorological Organization (WMO) meeting on CDA
defined the classification of weakly and strongly coupled data assimilation as well as their variations.
In this article, we follow the WMO definitions of CDA-related terminology.
Many CDA studies have already considered the coupled forecast initialization
on timescales from seasonal to decadal
(e.g. the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), ;
the National Oceanic and Atmospheric Administration
Geophysical Fluid Dynamics Laboratory (NOAA/GFDL), ).
At the Met Office, a weakly coupled data assimilation (WCDA) system has been developed
to improve the forecast skill from short range to seasonal timescales,
though this system is not yet operational.
It is based on using a coupled atmosphere–land–ocean–ice model to compute
the background states for separate atmosphere and ocean analyses
in a 6 h assimilation window.
Generally speaking, WCDA at the Met Office performed reasonably well,
providing results very similar to the uncoupled DA.
In that study, the authors identified two main problems in their implementation of WCDA:
the ocean SST diurnal cycle issue
and an erroneous coupled river runoff.
The latter issue led to degradation in the salinity fields around some river basins.
These results are nevertheless encouraging considering that the CDA system is new
and neither the atmospheric nor ocean data assimilation systems were adjusted as part of implementing CDA.
A CDA system was developed
at the European Centre for Medium-Range Weather Forecasts (ECMWF) to be used
for the global coupled reanalysis of the recent climate.
Their system consists of quasi-strongly coupled atmospheric and ocean DA using
the coupled atmosphere–ocean model to compute updated short-term coupled forecasts
during each outer-loop iteration
for the 4D-Var atmospheric and 3D-Var ocean DA systems.
They performed realistic CDA experiments and compared them with an uncoupled system.
The results of CDA were similar to the uncoupled DA,
with a small positive impact on the ocean temperature
and slightly improved atmospheric temperature near the surface, especially in the tropics.
The 10 d forecast skill scores for coupled atmospheric–ocean forecasts were mostly neutral,
with a small improvement in the eastern tropical Pacific.
The authors concluded that such a coupled system was a promising tool
for investigating CDA methodology.
They also pointed out further potential improvements of the CDA system.
First, they stated that direct assimilation of SST and sea ice observations
may improve the use of these data as well as that of other near-surface observations.
Second, coupled background error statistics with realistic representations of covariances
between the atmosphere and ocean are needed.
To explore the latter issue, examined aspects of strongly coupled DA by
using explicit cross-correlations between the atmosphere and ocean background error
estimated from an ensemble of coupled ocean–atmosphere models.
In comparison to their system with the coupled outer loop,
the use of explicit cross-correlations provides similar results.
In addition, the authors estimated that when a fully coupled ocean–atmosphere model
is used in the outer loop,
6 to 12 h of model integration are needed to synchronize
the uncoupled ocean and atmospheric increments from the inner loops.
The authors pointed out that a shorter time with strongly coupled DA, around 6 h, is needed to synchronize ocean increments
in the regions where the cross-correlations are large,
including the tropical and northern tropical Pacific and shallow mixed layer regions.
The required time is, however, longer at around 12 h in the midlatitudes or where the mixed layer is deep.
The shorter synchronization time may potentially play a positive role
in CDA systems with comparable DA windows,
introducing fewer initialization shocks into DA.
also developed a strongly coupled DA system using linearized
atmosphere–ocean balance operators in a simplified framework.
This work showed a positive impact on forecast skill, especially in the tropics.
Recently, developed another CDA implementation to be used for the purposes of NWP at ECMWF.
They showed that an NWP system may be degraded by model biases in the ocean component
of the coupled atmosphere–ocean model.
This is why they have chosen a weaker form of CDA than the system used for the coupled reanalysis described above.
The chosen method was similar to WCDA,
wherein the atmosphere and the ocean are coupled implicitly at a frequency of 24 h.
The interaction between the two components is not performed using a coupled model
but by using the analysis in one component to specify boundary conditions in another component
for the next 24 h cycle.
This system resulted in smaller errors of atmospheric temperature and humidity
in the regions from the surface up to 700 hPa.
The WCDA also resulted in smaller errors for SST in the tropics compared to uncoupled analyses.
However, the analysis increments within WCDA were significantly smaller than those within the uncoupled system
in the northern extratropics, while in the southern extratropics the two systems behaved very similarly.
At ECCC, a WCDA between the atmosphere and land surface systems has been running for many years ,
whereas the ocean–ice and atmospheric components have remained uncoupled.
The present study reports the development of a WCDA prototype between
the atmospheric and ocean DA components.
The chosen first prototype of WCDA at ECCC employs the same fully coupled atmosphere–ocean–ice model
that is used operationally for medium-range forecasts to compute coupled background states
for the atmosphere and ocean DA systems.
The aim of this approach is to investigate the impact of the evolving ocean temperature on the atmospheric DA
using existing independent DA components and with no additional tuning of the models.
The new WCDA system is assessed using realistic experiments based on systems very similar to those used operationally at ECCC.
The results of the new WCDA system are compared with the current uncoupled DA system.
The next section describes the individual DA and forecast components used in this study.
The ways these individual systems are linked together within
the complete forecast–analysis cycle are described for the
uncoupled and WCDA
approaches in Sect. and , respectively.
In Sect. , results from DA experiments are presented and analyzed.
Conclusions are given in Sect. .
Description of the ECCC atmospheric and ocean prediction components
In this section, we describe the DA schemes for the atmosphere, ocean, SST and sea ice,
followed by a description of the fully coupled NWP forecast model used at ECCC.
All DA components as well as the NWP models, coupled and uncoupled,
have already been validated and reported in numerous studies.
Here we briefly describe only the parameters related to the present study.
Atmospheric data assimilation
The atmospheric DA component used in this study is the same as the
Global Deterministic Prediction System GDPS: developed and used operationally at ECCC.
The computation of the background state during the analysis cycle is performed using
the Global Environmental Multiscale (GEM) atmospheric model
with a horizontal grid spacing of 25 km and 80 vertical terrain-following levels
with the model top at 0.1 hPa.
The turbulent surface heat and momentum fluxes are parameterized using stability functions described in and .
Graphical representation of 4D-EnVar analysis cycle during 24 h using IAU initialization.
The 4D-EnVar analyses are computed using a 6 h assimilation window centered
at 00:00, 06:00, 12:00 and 18:00 UTC (red dots on the timescale).
The GEM model (in coupled or uncoupled mode) 6 h integration
using an IAU initialization of the analysis increments (solid lines)
starts at 21:00, 03:00, 09:00 and 15:00 UTC (green dots on the timescale),
followed by 6 h computation of the background states for the next analysis (dotted lines).
The 21 blue dots show the atmospheric states, available hourly,
used as atmospheric forcing at 1 h frequency for
the NEMO–CICE model to compute coupled 24 h ocean background within WCDA (see text).
The last three forcing states of every 24 h cycle are taken from the computation of the background fields (white dots).
The DA method is a hybrid four-dimensional ensemble-variational (4D-EnVar)
using incremental analysis update (IAU) initialization .
The IAU implementation used with the GDPS system is illustrated in Fig. .
The hybrid approach combines four-dimensional ensemble covariances with the static error covariances
computed with so-called National Meteorological Center (NMC) method
to estimate the full spatio-temporal background error covariances over the 6 h assimilation time window.
The ensemble and static covariances are averaged with equal weights in the resulting full background error covariances.
The ensemble covariances are estimated from the ensemble of 256 uncoupled background states,
available hourly within the 6 h assimilation window, obtained from
the global ensemble Kalman filter (EnKF) being used operationally at ECCC
since 2005.
The EnKF version used in this study (4.1.1) is the one that was
operational at the time of the experiments.
This version was replaced by an updated version in September 2018.
The 4D-EnVar analysis increments are computed on a grid with a horizontal grid spacing of 50 km,
as in the EnKF system, on all 80 vertical levels.
In the current version of the GDPS,
the operationally assimilated data consist of
those from microwave and infrared satellite
sounders and imagers, scatterometers, radiosondes, aircrafts, wind profilers, land stations,
near-surface observations from ships and buoys, atmospheric motion vectors,
ground-based GPS sensors,
and satellite-based radio occultation instruments.
More details about the assimilated data may be found in .
The GEM model has been designed to be coupled with a land surface model; it is called ISBA:
the Interactions between Soil–Biosphere–Atmosphere scheme .
ISBA computes the surface heat and momentum fluxes over four surface types: land, glacier, water and sea ice.
The prognostic surface variables of the current surface model are computed for the land surface only,
which has its own DA system .
The land surface model and DA in both uncoupled and coupled DA experiments of this study
are similar to what is currently used operationally.
The definition of water and sea ice surfaces requires information from the external SST and sea ice DA components, as
described in Sect. and , respectively.
Ocean data assimilation
The ocean DA component used in this study
is the same as the current operational Global Ice–Ocean Prediction System GIOPS:.
The numerical model used is the NEMO–CICE coupled ocean–ice model based on NEMO
(Nucleus for European Modelling of the Ocean) version 3.1.3
and CICE (Community Ice CodE) version 4.0 .
The NEMO version used here has a global 1/4∘ horizontal ORCA grid and 50 vertical levels ranging
from the ocean surface to the ocean bottom with spacing increasing from 1 m at the surface to 500 m near the bottom.
The thermodynamic component of CICE computes the growth and melt of snow and ice
as well as the vertical temperature profile using four ice layers and one snow layer.
The ocean upper boundary conditions are the turbulent surface latent, sensible,
incoming and outgoing longwave and shortwave radiation fluxes.
The ocean DA system used is the Système d'Assimilation Mercator version 2 (SAM2) .
The SAM2 DA algorithm is the singular evolutive extended Kalman (SEEK) filter derived from the Kalman filter .
The background error covariances are estimated using an ensemble of multivariate three-dimensional anomalies
derived from a multiyear hindcast simulation .
The version used in this study is GIOPS v2.2.3, corresponding to the currently operational system.
The SAM2 DA consists of two successive DA cycles.
First, in a weekly cycle, in situ data, SST and satellite altimetry data are assimilated.
The in situ data are derived from multiple sources including the Argo floats ,
as well as ships, moorings and instrumentation on sea mammals.
The satellite altimetry data are obtained from a product combining anomalies of the sea level with a mean dynamic topography.
The sea level anomalies are provided by Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO),
SSALTO/DUACS near-real-time data including Jason-2, and CryoSat-2 and SARAL/AltiKa satellite data.
The CNES-CLS09 mean dynamic topography used is from .
Second, in a daily assimilation cycle, only SST data are assimilated.
The assimilated SST is itself a gridded analysis computed within the operational SST DA based on optimal interpolation (OI)
as described in Sect. .
It is designed to reduce initialization shocks for coupled forecasts by imposing
consistent surface conditions on the atmosphere and ocean during their separate assimilation cycles.
The assimilation of an SST OI analysis by an ocean analysis system is also fairly commonplace within the operational oceanographic community (e.g. by the Global Ocean Forecasting system within the Copernicus Marine Environmental Monitoring System; ).
This approach has the benefit that it provides a pretreatment (or superobbing)
of high-resolution SST observations, allowing for the assimilation of more satellite SST datasets.
Since the coverage of satellite SST data is quite complete,
data gaps do not produce a significant problem compared to the advantages of this approach.
Within this second DA cycle, the sea ice analysis
produced by the system described in Sect.
is also used to reinitialize the model
ice concentration each day.
Sea surface temperature data assimilation
The SST DA system is described in detail in and .
The version used in this study is the operational SSTv1.2.2, which is currently employed
in the operational implementation of the atmospheric GDPS system (see Sect. ).
The DA approach used in the system is the OI method
producing daily analyses considered to be valid at 00:00 UTC.
The assimilated data are collected during the period of 24 h before this valid time.
The SST OI assimilates data from multiple in situ platforms (drifting and moored buoys, ships)
and Advanced Very High Resolution Radiometer data provided by the
US Naval Oceanographic Office that are
derived following the multichannel SST (MCSST) approach .
The SST DA system does not employ a numerical forecast model.
Instead, it uses the previous analysis state computed 24 h earlier
as the background state for the assimilation.
The assimilation of the SST data from multiple satellite instruments along with the in situ data is discussed in detail in .
An important aspect of the system is the estimation and removal of observation biases.
Indeed, the infrared radiometer measures the temperature within the conductive diffusion layer at a depth of ∼10–20 µm,
or the so-called skin temperature.
The microwave radiometer retrieves the sub-skin temperature at the base of the laminar layer at around 1 mm of depth.
All these data need to be reconciled with the in situ temperature usually measured at a depth of ∼2 m.
On the other hand,
the current weather prediction system requires an SST field to be kept fixed through 24 h.
To this end, the SST at a depth at which the effect of the diurnal cycle is negligible was required.
Since the in situ observations are located sufficiently deep,
the data from different satellite instruments are adjusted to the in situ data first.
Second, a so-called foundation SST, the temperature at a depth without a diurnal cycle, is estimated.
The estimation is performed in two stages.
First, the background state,
which is the anomaly field of the foundation SST, is computed
by subtracting from the analysis of a previous state a precomputed monthly mean climatology
interpolated in time.
After the analysis state of the foundation SST anomaly has been computed,
the climatological field interpolated to the current day is added to it to obtain the SST analysis state.
The output of this system is a daily analysis of the foundation SST field
on an uniform 0.2∘×0.2∘ latitude–longitude grid.
This field is used within both the GDPS (Sect. )
and GIOPS (Sect. ) DA components.
Sea ice data assimilation
The sea ice concentration DA system
is based on the 3D-Var DA method implemented on a global domain at ∼10 km resolution.
As for the atmospheric DA component described in Sect. ,
the sea ice analyses are computed every 6 h at 00:00, 06:00, 12:00 and 18:00 UTC.
Similar to the SST analysis, the sea ice analysis system was originally developed
using various sources of satellite data that generally have good combined spatial coverage globally every 6 h.
This system was developed before our center became active in using sea ice models,
and therefore it was for practical reasons that it does not use a model within the assimilation cycle
but instead uses the previous analysis produced 6 h earlier
as the background state for the assimilation.
The 3D-Var sea ice analysis assimilates passive microwave satellite observations
from the Special Sensor Microwave Imager (SSM/I),
the Special Sensor Microwave Imager/Sounder (SSMIS)
and the Canadian Ice Service (CIS) manual analyses .
Details about how these datasets were treated within the 3D-Var DA may be found in .
The sea ice concentration analyses are used to initialize the atmospheric (see Sect. )
and the ice–ocean (Sect. ) forecast models to compute the background states
for both uncoupled and coupled DA systems described in Sect. .
Coupled weather forecast model
In this section, the model used to produce medium-range forecasts in both the uncoupled and WCDA systems
(see Sect. and , respectively) is described.
The system used in this study is the fully coupled atmosphere–ocean–ice GEM–NEMO–CICE model
that was operationally implemented at ECCC for global NWP in November 2017 .
This system is used to produce 10 d
coupled atmosphere–ocean–ice forecasts initiated at 00:00 and 12:00 UTC.
The atmospheric GEM model initial conditions are the corresponding 00:00 and 12:00 UTC
atmospheric analyses (see Sect. ) using the IAU initialization.
The GEM model version used in this study is similar to the model described in Sect.
except for the modified ocean interface
whereby the contributions from the ice–ocean model are transferred to the atmospheric model every model time step.
The coupled NEMO version is 3.1.3 and CICE is 4.0 as described in Sect. .
However, the models are launched in a coupled mode that
consists of a flux coupling approach described in detail in and .
This approach aims to simulate the interactive and consistent transfer
of heat, moisture and momentum between the atmospheric and ocean–ice models.
It is based on common atmospheric, oceanic and sea ice state variables.
First, the atmospheric model computes downward radiative fluxes and state variables
and then transfers this information to the ocean–ice model.
Second, these fluxes and variables are used by the ocean model
to compute turbulent upward surface atmospheric fluxes
using the same formulation as used within the atmospheric GEM model .
The turbulent surface fluxes over the ice-covered areas are computed within the CICE model
using stability functions described in .
Finally, the surface fluxes are transferred to the atmospheric model
along with the SST and sea ice at every model time step of 15 min.
The time stepping of the coupled atmosphere–ocean–ice model is implemented such that
the atmospheric model moves forward one step ahead of the ocean–ice model prior to sending its variables.
The initial conditions for the NEMO ocean model are obtained
from the daily SAM2 analyses at 00:00 UTC (Sect. , ) for both forecasts
launched at 00:00 and 12:00 UTC.
The 00:00 and 12:00 UTC initial conditions for the CICE ice model are obtained
from the 3D-Var DA (Sect. ) computed at 18:00 UTC of the previous day,
which normally assimilates more data than the analyses computed at 00:00, 06:00 and 12:00 UTC.
Description of the complete forecast–analysis cycles
In the present section, the uncoupled DA (referred to as UNCPL)
and WCDA (referred to as CPL) configurations of the complete forecast–analysis cycles are discussed.
UNCPL is similar to the combined set of uncoupled systems currently used
at ECCC for the operational production of forecasts.
This system will be used in this study as a reference to evaluate the performance of the new CPL system.
Both systems use the DA and model components described
in Sect. .
The differences in design between UNCPL and CPL are in the assimilation
cycles, whereas initial conditions from both UNCPL and CPL are used to initialize
fully coupled atmosphere–ocean–ice 10 d forecasts (Sect. ).
Uncoupled data assimilation
The graphical scheme of the uncoupled DA cycle is shown in Fig. .
The atmospheric 4D-EnVar (Sect. ) analyses are computed four times per day
at 00:00, 06:00, 12:00 and 18:00 UTC.
The uncoupled atmospheric GEM model is initialized using IAU during 6 h integration centered at the analysis time,
followed by the 6 h GEM model integration to compute the background state
for the next atmospheric DA (see Fig. ).
The GEM model forecast is computed using the SST and sea ice fields
specified by the separate DA systems described in Sect. and , respectively.
The GEM model version used in this system is the same as described in Sect. .
The uncoupled DA system (UNCPL) scheme.
The atmospheric 4D-EnVar DA component computes analyses every 6 h.
The SST OI DA component computes daily analyses valid at 00:00 UTC,
which initialize the atmospheric GEM model and
are assimilated using the daily SAM2 ocean DA component at 00:00 UTC.
The 3D-Var ice analyses are computed every 6 h and are used to
initialize the uncoupled 6 h atmospheric and 24 h oceanic uncoupled forecasts.
The uncoupled atmospheric analyses are propagated
in space and time using the GEM model.
The uncoupled ocean analyses are propagated using the NEMO–CICE model.
The weekly SAM2 ocean DA system for in situ data and satellite altimetry
is used in the same way in both UNCPL and CPL systems and is not shown.
The daily ocean SAM2 DA (Sect. ), assimilating only SST daily mean data,
is computed at 00:00 UTC.
The ocean–ice NEMO–CICE model is initialized using IAU during a 24 h model integration
starting 24 h before the current analysis time, followed
by another 24 h NEMO–CICE model integration (see Sect. ) to compute
the background state for the next daily ocean DA.
The ocean–ice NEMO–CICE model forecast during 24 h is run in uncoupled mode
using the atmospheric forcing fields from the GEM model forecast at 3 h frequency
started at 00:00 UTC.
The 3D-Var sea ice DA (Sect. ) computes analyses every 6 h,
providing the ice concentration field used
for the short-term forecasts used to produce the atmospheric background state.
The 3D-Var sea ice analysis computed at 18:00 UTC is used for initializing the 24 h ocean–ice
forecast used as the background state for the following day.
The SST OI analysis (Sect. ) computed at 00:00 UTC provides the static SST field used for the
following four 6 h atmospheric forecasts used as the background states during 24 h.
The same SST analysis is also assimilated into the SAM2 daily ocean DA system (see Sect. ).
Weakly coupled data assimilation
The graphical scheme of the CPL cycle, combining coupled 6 h atmospheric and 24 h ocean DA systems,
is shown in Fig. .
The atmospheric 4D-EnVar analyses (Sect. ) are computed every 6 h (see Fig. ) exactly
as in the UNCPL system described in the previous section.
However, the fully coupled atmosphere–ocean–ice model (see Sect. ) is used within CPL
to compute the coupled atmospheric background states for the atmospheric DA.
WCDA system (CPL) scheme.
The atmospheric 4D-EnVar DA component computes analyses every 6 h.
The SST OI DA component computes daily analyses valid at 00:00 UTC,
which are assimilated using the daily SAM2 ocean DA component at 00:00 UTC.
The 3D-Var sea ice analyses are computed every 6 h.
The 3D-Var sea ice analysis computed at 18:00 UTC
provides the initial condition for
the computation of fully coupled atmosphere–ocean background states
at 00:00, 06:00, 12:00 and 18:00 UTC.
The separate atmospheric and ocean analyses are propagated
in space and time using the fully coupled GEM–NEMO–CICE model.
Only the atmospheric DA component of the CPL system explicitly uses
the fully coupled atmospheric–ocean–ice model to compute the background states.
To compute the observation-minus-background difference,
the SAM2 ocean DA component directly uses the ocean–ice model launched in a forced mode,
i.e. using precomputed atmospheric forcing fields.
However, by saving the atmospheric fields from the 6 h coupled forecasts
and using these to force the ocean model,
this is equivalent to
the explicit use of the fully coupled atmosphere–ocean–ice model.
Using the precomputed atmospheric forcing fields from the coupled ocean–atmosphere forecasts allows us to implicitly compute coupled ocean background states without modifying the SAM2 code.
Preliminary experiments showed that
the use of the precomputed atmospheric forcing from the fully coupled model every hour
gives results similar to the forcing changing every model time step
(the ocean–ice model time step is 15 min in our experiments).
So the frequency of the atmospheric forcing
has been set to 1 h.
Hence, in order to compute coupled 24 h background states for the ocean DA,
the CPL system first performs four atmospheric 6 h DA cycles as shown in Fig. .
The coupled atmospheric states from the coupled atmosphere–ocean–ice model integrations
during the application of IAU are stored every 1 h during this stage.
To cover the whole 24 h cycle, the last three states are taken from the coupled background fields
because the states during the use of IAU at 22:00, 23:00 and 00:00 UTC are not yet available
in the current 24 h DA cycle.
Once the entire 24 h period of atmospheric forcing (following the approach just described) is available,
the ocean DA starts by integrating the ocean–ice model over the 24 h period
to compute the observation-minus-background differences for SST.
From these differences, the daily SAM2 ocean DA then computes analysis increments.
The ocean analysis increment is then used to rerun the ocean–ice model over the same 24 h period
using the same atmospheric forcing.
This provides the initial conditions for the next 24 h cycle.
As in UNCPL, the 3D-Var sea ice analysis (Sect. ) is computed every 6 h.
However, only the analysis at 18:00 UTC is used to initialize the computation
of four 6 h fully coupled atmosphere–ocean forecasts used as background states during 24 h
exactly as it is implemented in the coupled weather forecast model (see Sect. ).
The SST OI analysis (Sect. ) is computed at 00:00 UTC
and assimilated by the daily ocean SAM2 DA component as in the UNCPL system.
Comparison experiments
The experiments are conducted for the period of August–September 2017.
The verification statistics shown in this section are computed between 15 August and 20 September
during which a series of tropical Atlantic hurricanes occurred.
Two 5 d forecasts per day at 00:00 and 12:00 UTC are carried out over this period.
The atmospheric and ocean–ice initial conditions for all experiments are taken from the DA
systems described in Sect. and .
The comparison study focuses on the differences in the atmospheric and ocean forecasts and the analyses
produced by the CPL and UNCPL systems.
Differences between these two systems are expected for the forecasts of SST and near-surface layers
in both atmosphere and ocean models.
(a) Observation-minus-analysis (OmA) statistics:
mean (a) and standard deviation (b)
using 6 h forecasts.
Mean and standard deviation
for the brightness temperature (K) as seen by the AQUA AIRS channel 950.
The statistics are computed for UNCPL (blue) and CPL (red)
in the northern extratropics region between 30 and 60∘ N.
The use of a coupled model between the atmosphere and the ocean–ice to compute the background state
may lead to changes in both atmosphere and ocean DA.
Concerning the atmosphere, these changes may be seen, for example,
using satellite radiances that are sensitive to the temperature of the ocean.
Such instruments measure radiance within the atmospheric window;
i.e. the sensitivity to the atmospheric temperature and humidity is low,
which allows the emission from the ocean surface temperature to be measured.
In order to illustrate the impact of the evolving ocean surface temperature
on the atmospheric DA, the evolution of
the observation-minus-analysis (OmA) and
the observation-minus-forecast (OmF) biases as well as the standard deviation
are computed for the brightness temperature (TB) of the AQUA AIRS channel 950
(see Figs. and , respectively).
6 h forecasts are used to compute the OmF biases and standard deviation.
Generally, both OmA and OmF biases for this atmospheric window channel for UNCPL and CPL are similar.
However, the CPL experiment results in smaller OmA and OmF standard deviations than UNCPL.
Overall relative to UNCPL, the CPL standard deviations are reduced by 2.7 % for OmA and 1.9 % for OmF.
For the OmA and OmF biases,
we clearly see a 24 h period,
so it is likely related to the diurnal cycle.
However, for the standard deviation,
it looks like a 12 h variation,
so it is not due to the diurnal cycle.
This effect is probably related to the
data coverage.
Observation-minus-forecast (OmF) statistics:
mean (a) and standard deviation (b)
using 6 h forecasts.
Mean and standard deviation
for the brightness temperature (K) as seen by the AQUA AIRS channel 950.
The statistics are computed for UNCPL (blue) and CPL (red)
in the northern extratropics region between 30 and 60∘ N.
Such improvements in the OmF–OmA statistics for atmospheric window channel radiances
may also be reflected in improvements of the medium-range atmospheric forecast.
Figure shows the difference in the standard deviation of the forecasted atmospheric temperature
against the mean analysis (mean between the two experiments: CPL and UNCPL)
for different pressure levels between 1000 and 10 hPa as a function of the forecast lead time.
Red shows the lead times and pressure levels at which CPL performs better
(i.e. has a lower standard deviation) than UNCPL, and blue shows the converse.
The score shown in the figure is computed in the northern extratropics region in
the latitude band between 20 and 60∘ N.
In most cases, CPL performs slightly better than UNCPL.
A statistically significant difference with confidence above 90 %
is only observed for the near-surface air temperature at around 1000 hPa for the 12 and 36 h forecasts
(the difference is statistically insignificant elsewhere).
Similar forecast scores computed for the geopotential height and specific humidity
also show slightly better performance for CPL
but with no areas in which the improvement is statistically significant (not shown).
The impact of CDA on forecast scores computed for the wind field is rather neutral (not shown).
Similar results were also obtained when the ERA5 reanalysis is used instead of the mean analysis (also not shown).
Difference in the standard deviation of the air temperature (∘C)
against the mean analysis as a function
of forecast lead time.
The statistics are computed for CPL and UNCPL
in the northern extratropics region between 20 and 60∘ N.
Positive values (red) mean that the standard deviation produced by CPL is smaller,
whereas negative values (blue) mean the converse.
Numbers show the areas where the difference between CPL and UNCPL
is statistically significant with a confidence level above 90 %.
(a) Standard deviation (solid curves) and bias (dashed curves) growth (∘C) as a function
of forecast lead time for the air temperature at 1000 hPa.
The statistics are computed for the CPL (red) and UNCPL (blue)
against the mean analysis
in the northern extratropics region between 20 and 60∘ N.
(b) The difference in the standard deviation error in percent between CPL and UNCPL.
Green indicates the number of samples used to compute the statistics.
Figure helps to highlight the better performance of CPL
for the forecast of near-surface air temperature over the same northern extratropics region.
Figure a shows the standard deviation and bias of the air temperature at 1000 hPa
against the mean analysis
as a function of the forecast lead time.
Figure b shows the difference between the standard deviations of the CPL and UNCPL.
The CPL experiment results in a standard deviation which is about 2 % smaller than
the corresponding value from the UNCPL experiment.
However, CPL results in a larger negative bias in the same area.
Evolution of sea surface temperature OmF standard deviation (a, b, c)
and mean OmF (d, e, f) (∘C) with respect to SST data
assimilated within the separate SST DA component.
The statistics are computed for UNCPL (blue curves) and CPL (red curves) in three latitude bands:
southern extratropics between 20 and 60∘ S (a, d),
tropics, between 20 and 20∘ N (b, e), and
northern extratropics between 20 and 60∘ N (c, f).
The statistics are computed for the 12 h forecast and the corresponding data.
Let us now examine the quality of SST forecasts from the CPL and UNCPL experiments.
Figure shows the evolution of OmF standard deviation and mean OmF for SST
computed with respect to the original satellite and in situ observations
that were used within the SST DA system described in Sect. .
The OmF statistics are computed for the 12 h coupled forecasts
produced using the CPL and UNCPL initial conditions in three different latitude bands:
southern extratropics between 20 and 60∘ S,
tropics between 20∘ S and 20∘ N, and
northern extratropics between 20 and 60∘ N.
The OmF standard deviations produced by CPL are systematically lower than those produced by UNCPL
in all three regions, whereas the mean OmF produced by CPL is sometimes larger.
The differences for the mean OmF are small
and also not systematically in favor of one experiment over the other
(as opposed to the difference for the standard deviation of OmF).
Due to this variation in the differences for the mean OmF
and the relatively short length of the experiments,
we are not confident in the statistical significance of the differences.
Standard deviation (a, b, c) and bias (d, e, f) (K)
with respect to the ERA5 reanalysis daily mean SST fields as a function of forecast lead time.
The statistics are computed for UNCPL (blue curves) and CPL (red curves) in three latitude bands:
southern extratropics between 20 and 60∘ S (a, d),
tropics between 20∘ S and 20∘ N (b, e), and
northern extratropics between 20 and 60∘ N (c, f).
Figure shows the SST standard deviation and bias
from the coupled medium-range forecasts produced using the CPL and UNCPL initial conditions
in the same three latitude bands as in the previous figure.
The standard deviation and bias are computed as a function of the forecast lead time
with respect to the daily mean OSTIA product used in the ECMWF ERA5 reanalysis
generated using Copernicus Climate Change Service information.
This product was chosen to evaluate the forecasts from our experiments because it can be considered
an independent dataset.
Another high-quality SST product,
the Group for High Resolution SST (GHRSST) Multi-Product Ensemble (GMPE) system ,
was not chosen because it partially uses the same SST analyses from ECCC as described in Sect. .
The standard deviations are similar for both experiments in all three latitude bands,
with slightly smaller values in the tropics for CPL.
The bias produced by CPL is smaller in the tropics and northern extratropics
and slightly bigger in the southern extratropics.
The coupled forecasts represent the diurnal cycle to some extent for the SST,
which is not captured in the ERA5 SST analyses (since it is a foundation SST).
Therefore, for the northern extratropics the mean difference in SST has a strong daily cycle
since the Pacific Ocean dominates the oceanic surface area at these latitudes,
which is mostly day at 00:00 and night at 12:00 UTC.
In contrast, the other regions have more even oceanic coverage
at all longitudes (especially for the southern extratropics)
and therefore have a more even coverage of day and night at both 00:00 and 12:00 UTC.
OmF standard deviation (a) and mean (b) with respect to the gridded foundation SST field
from the ocean SAM2 DA component for the Puerto Rico XBT region
situated within the latitude–longitude box defined by [35, 65∘ W] and [25, 35∘ N].
The statistics are computed using 24 h forecasts.
Similar error statistics for SST can be computed within the ocean DA system described in Sect. .
Figures and compare the SST OmF standard deviation and bias computed using 24 h forecasts
within the CPL and UNCPL experiments.
The error statistics are computed relative to the gridded foundation SST field
obtained from the SST DA that was assimilated by the daily SAM2 ocean DA as explained in Sect. .
The use of the analysis field to compute the standard deviation and biases affects the spatial sampling
compared to OmF statistics against the SST satellite and in situ data shown in Fig. .
The figures show two typical OmF plots computed
in the Puerto Rico XBT region, showing the performance in the western tropical and northern extratropical Atlantic Ocean,
and the Niño3 region in the tropical Pacific Ocean.
The CPL results in generally smaller OmF biases and standard deviation errors in most regions.
OmF standard deviation (a) and mean (b) with respect to the gridded foundation SST field
from the ocean SAM2 DA component for the Niño3 region situated within the latitude–longitude box defined by
[150, 90∘ W] and [5∘ S, 5∘ N].
The statistics are computed using 24 h forecasts.
Difference (W m-2) between the standard deviation of UNCPL and CPL
for the turbulent surface sensible heat flux computed using 12 h coupled forecasts during September 2017.
The changes in the SST may modify the turbulent surface heat flux forecasts.
To qualitatively evaluate the impact of CDA on the fluxes,
the standard deviations for the turbulent surface sensible heat flux were computed
using short 12 h coupled forecasts produced with the UNCPL and CPL initial conditions during September 2017.
Figure shows the difference between the two standard deviation fields.
Positive values (red) mean that the standard deviation of the flux from the UNCPL experiment
is bigger than the standard deviation from the CPL experiment, whereas negative values (blue) mean the converse.
The decrease in the standard deviations of the surface sensible heat flux
reflects a better accordance between the near-surface atmospheric temperature and the SST.
While the same gridded SST OI analysis is assimilated in the ocean
and used for the surface boundary condition in UNCPL,
differences remain between the SST of the ocean analysis and the OI analysis.
The OI analysis is assimilated with a 0.3 ∘C observation error by the ocean analysis,
and thus differences of up to 1.0 ∘C can be found.
These differences are especially apparent in energetically active areas of the ocean
(due to small-scale eddies not captured in the OI analysis)
as well as due to the presence of cyclones (e.g. due to cold wakes).
As a result, surface fluxes in UNCPL forecasts will reflect this imbalance in initial conditions.
In most regions, the impact of CDA on the surface sensible heat flux is neutral
except the northern extratropical Atlantic,
where a series of tropical Atlantic hurricanes occurred, as well as in the northern extratropical Pacific.
In these regions, positive differences of 8–16 W m-2 and negative differences of 4–8 W m-2 are observed,
though the number of positive differences is bigger.
Similar spatial structures but with smaller values are observed
when the same quantities are computed for the surface latent heat flux (not shown).
It was noted during the evaluation of the fully coupled atmosphere–ocean–ice model
that interactions on the surface interface resulted in a reduced latent heat flux
due to the formation of cold wakes associated with cyclones, leading to reduced intensification.
Inclusion of the fully coupled atmosphere–ocean–ice model in the computation
of background states improves the representation of these interactions in the analysis,
resulting in a further decrease in the variance of fluxes
(i.e. likely due to the reduced intensification of cyclones).
Difference between the UNCPL and CPL (24 h forecasts) RMSE (∘C)
with respect to the Argo ocean temperature measurements in September 2017.
Positive values (red) indicate that the RMSE produced by CPL is smaller,
whereas negative values (blue) mean the converse.
(a) The RMSE difference for ocean temperature at 2.5 m of depth.
The grey areas show the regions where Argo measurements were not taken.
(b) The temperature vertical section through 1.5∘ N latitude
in the eastern tropical Pacific Ocean between 120 and 100∘ W.
Difference between the UNCPL and CPL (24 h forecasts) RMSE (psu)
with respect to the Argo ocean salinity measurements in September 2017.
Positive values (red) indicate that the RMSE produced by CPL is smaller,
whereas negative values (blue) mean the converse.
(a) The RMSE difference for ocean salinity at 2.5 m of depth.
The grey areas show the regions where Argo measurements were not taken.
(a) The salinity vertical section through 6.5∘ N latitude
in the tropical Atlantic Ocean between 40 and 15∘ W.
Finally, let us examine the impact of the WCDA on the subsurface ocean circulation.
Figures and show the difference between
the root mean square error (RMSE)
for the monthly mean 24 h forecasts produced using UNCPL and CPL initial conditions
relative to the Argo ocean temperature and salinity, respectively.
The data used for the comparison are gridded fields using a global 1/2∘ horizontal grid
and 58 vertical levels obtained from
the “Monthly mean datasets of the mean and annual cycle of temperature, salinity, and steric height in the global
ocean from the Argo Program” downloaded from http://sio-argo.ucsd.edu/RG_Climatology.html (last access: 23 January 2019).
Positive values (red) indicate that the RMSE of CPL is smaller,
whereas negative values (blue) mean the converse.
Figures a and a show the RMSE differences for
ocean temperature and salinity at 2.5 m of depth, respectively.
Figures b and b show vertical sections of the differences between the RMSE from the UNCPL and CPL experiments.
The vertical section for temperature is plotted through 1.5∘ N latitude between 120 and 100∘ W.
In most areas, CPL results in smaller temperature errors than UNCPL.
The biggest differences for ocean temperature between the UNCPL and CPL experiments
are observed in the eastern tropical Pacific,
where the biggest positive differences of about 0.45 ∘C form a front
with the biggest negative values of about -0.5∘C.
Also for the vertical section, the errors produced by CPL are generally smaller
in most areas between the ocean surface and 80 m of depth.
Below this depth, the difference between the CPL and UNCPL experiments is negligible.
For ocean salinity (Fig. ),
both CPL and UNCPL result in a similar RMSE except in the tropics,
where CPL produces a lower RMSE than UNCPL.
The biggest values of the RMSE differences, around 0.55 psu, are observed in the tropical Atlantic,
where the vertical section of the RMSE differences for ocean salinity is shown
through 6.5∘ N latitude between 40 and 15∘ W (Fig. b).
In this area CPL produces a lower RMSE for salinity between the surface and 20 m of depth.
The differences in salinity between the UNCPL and CPL experiments are negligible below 70 m of depth.
Conclusions
A WCDA system between the atmosphere and ocean DA components is implemented and evaluated in this study.
The first prototype of the WCDA is built on the existing
components of the NWP and operational ocean–ice prediction systems that have been previously run
as independent uncoupled DA systems.
As the NWP system requires SST and ice concentration fields
and the quality of ocean–ice prediction depends on atmospheric forcing,
the transition from uncoupled to strongly coupled DA
should be smooth and gradual in order to not degrade the quality
of the existing atmospheric and ocean–ice prediction systems.
The first step towards CDA was to replace the uncoupled models used to compute the background state for each DA
with the fully coupled atmosphere–ocean–ice model within separate atmosphere and ocean DA components.
The ocean community model NEMO has already been coupled with atmospheric models to perform WCDA in multiple studies.
However, the atmospheric GEM model and the 4D-EnVar DA were never tested before in a coupled framework.
The present study showed that the use of the coupled atmosphere–ocean–ice model
to compute the background states for separate atmospheric and ocean DA systems
has a generally neutral to positive effect on the 5 d atmospheric forecasts.
However, the verification scores from WCDA are significantly better up to day 4
in the near-surface atmospheric layers in the tropics and northern extratropics.
WCDA also leads to better agreement between the near-surface atmospheric temperature and the SST,
locally decreasing the variability of turbulent surface heat fluxes compared to uncoupled DA.
Such encouraging results are obtained using the same configuration of the fully coupled atmosphere–ocean–ice model
that was used operationally within the NWP system with no additional tuning of the model
and allows one to explore further aspects of stronger coupling.
The improvement in the OmF standard deviation error
for the near-surface air temperature is accompanied by a slight increase in the bias
for the same forecast lead times and pressure levels.
It may be related to the current formulation of the error covariances that do not include
cross-correlation terms between different DA components.
Another positive result is that the quality of ocean forecasts for SST and salinity
with respect to both standard deviation errors and biases is improved
when using the coupled background states for the ocean DA.
This is noted by smaller verification errors for the ocean temperature and salinity, especially in the tropics.
This result is obtained despite the fact that the ocean DA, as the atmospheric DA, does not employ the cross-correlation error covariances.
Longer experiments are needed to further explore this issue.
The test period of 2 months used here might also be
too short to see major changes in the ocean component.
Thus, the results presented here should be viewed in this context.
As for the different impacts of WCDA in experiments carried out by the ECMWF and Met Office,
there could be multiple reasons for starting from the most obvious, such as differences in coupling strategies, models, data assimilation methods and assimilated observations.
However, what is clearly different in our system with respect to others is the ocean data assimilation system that uses two DA cycles, one with a weekly assimilation time window and the other with a daily window.
As stated in the paper, the weekly DA cycle is not affected by the coupling process and will therefore prevent the deep ocean state from diverging between the UNCPL and CPL systems.
In fact, every 7 d, the ocean is restarted from the same uncoupled initial conditions in both the UNCPL and CPL systems.
This certainly affects the results, especially in the ocean, keeping it relatively close to the uncoupled solution.
The small positive impact of WCDA on the ocean forecast that we observe is likely attributed to the better consistency between the ocean and atmosphere when using the coupled analysis, which reduces the initial shocks in the coupled forecasts.
In the current design of this first WCDA prototype,
the daily ocean DA is mainly constrained by uncoupled SST and ice concentration analyses
that do not rely on numerical models to compute background states.
This weakens the degree of coupling because the daily initial condition for the whole atmosphere–ocean–ice system
remains close to the uncoupled trajectory,
and a certain time is needed to synchronize such initial conditions with the coupled model trajectory.
The next step towards a stronger coupling would be to modify
the SST and sea ice concentration analyses such that they operate within a 6 h cycle and use coupled background states.
The transition from a daily to the 6 h SST analysis will require a new bias correction scheme for the SST data
in order to properly estimate the ocean temperature diurnal cycle.
In addition, the purely technical step of using integrated software for the atmosphere, SST and sea ice analysis
will be necessary as an initial step before exploring stronger coupling by
introducing background error cross-covariances between atmosphere and ocean components.
This system may also be extended to perform the analysis for the whole ocean mixed layer, not only SST,
every 6 h.
These 6 h analyses may replace the current daily ocean DA system.
This is the general orientation of future work.
Code and data availability
The codes, scripts and data used in this paper are available for the topical editor and anonymous reviewers.
Author contributions
SS, MB, SL and GS were responsible for the concept.
SS contributed to writing and original draft preparation.
MB, LG and SL contributed to writing, review and editing.
SS, EL, FR, DS-C and J-MB were responsible for software.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to acknowledge Pierre Pellerin, Pierre Koclas, Nicolas Gasset,
Jean-François Caron, Kristjan Onu, Mateusz Reszka, Kamel Chikar, Sylvain Heilliette,
Charles Creese, Richard Ménard and many others for fruitful discussions
and collaborative work on common software tools.
The authors also wish to thank the topical editor, Sophie Valcke, and two anonymous referees for the constructive review.
Review statement
This paper was edited by Sophie Valcke and reviewed by two anonymous referees.
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