Global ocean reanalyses combine in situ and satellite ocean observations with
a general circulation ocean model to estimate the time-evolving state of the
ocean, and they represent a valuable tool for a variety of applications,
ranging from climate monitoring and process studies to downstream
applications, initialization of long-range forecasts and regional studies.
The purpose of this paper is to document the recent upgrade of C-GLORS
(version 5), the latest ocean reanalysis produced at the Centro
Euro-Mediterraneo per i Cambiamenti Climatici (CMCC) that covers the
meteorological satellite era (1980–present) and it is being updated in
delayed time mode. The reanalysis is run at eddy-permitting resolution
(
Ocean retrospective analyses (or simply, reanalyses, or ocean syntheses) combine available in situ and satellite observations with an ocean general circulation model (OGCM) forced by atmospheric reanalyses by means of advanced data assimilation techniques, with the aim of estimating the state of the ocean in the past few decades (Balmaseda et al., 2015). Such a method implicitly ingests not only the information that comes from the global ocean observing system, but also the global meteorological observing system through the atmospheric forcing, and our knowledge of the ocean physics and dynamics through the OGCM.
Production of ocean reanalyses was originally fostered by the need to monitor the climate change signature on the ocean, for instance in terms of heat content evolution (Carton and Santorelli, 2008). Additionally, long-term (e.g. seasonal, decadal) prediction systems require proper initial conditions for the ocean, because of the dominant effect of the ocean initial conditions on the predictability over long timescales (e.g. Balmaseda and Anderson, 2009). For this reason, ocean reanalyses are a crucial requirement for climate predictions, which further encourages the improved use of observations along time. Reanalyses can be produced either in delayed or near real-time mode. The former mode refers to the production and update process that is generally performed, occasionally when all reprocessed input observation datasets are available; while the latter refers to a continuous update process, possibly using real-time observations, generally adopted when the reanalysis serves the purpose of initializing an operational long-range prediction system (e.g. Zuo et al., 2016).
During the last decade, the maturity of ocean reanalyses has been shown through many validation studies (Stammer et al., 2010; Xue et al., 2012; Storto et al., 2016c), which supported the idea that reanalyses provide useful information at least for the upper ocean (top 700 m). Meanwhile, the ocean observing system has notably evolved with the implementation of the Argo floats network, thanks to which an unprecedented sampling of the subsurface temperature and salinity was achieved.
Regional ocean simulations or analysis systems need open boundary conditions at the domain boundaries, which may be provided by ocean reanalyses (e.g. Sotillo et al., 2015). Additionally, downstream applications (e.g. biogeochemical, fishery and larval dispersal models) require physical fields as input, preferably from an ocean reanalysis (e.g. Lazzari et al., 2012; Meliá et al., 2013).
Improvements in ocean reanalyses are also required in the context of producing Earth system reanalyses for the atmosphere and ocean. “Strongly coupled” data assimilation methods are active research topics that will likely improve the use of observations for a variety of applications such as short- and long-term predictions (Laloyaux et al., 2015) and climate reanalyses (e.g. Dee et al., 2014). This implies that the optimal configuration of ocean retrospective analyses will benefit not only ocean applications, strictly speaking, but also Earth system assimilative experiments.
All the applications listed above clearly motivate developments and
production of ocean reanalyses with possibly short delays of dissemination
beyond real time. The Fondazione CMCC (Centro Euro-Mediterraneo sui
Cambiamenti Climatici) has devoted efforts in the last decade to build a
state-of-the-art ocean physical reanalysis system. First reanalyses were
produced at relatively coarse resolution (about 2
C-GLORS was released through the MyOcean portal (now at
The article is structured as follows: Sect. 2 describes the reanalysis system focussing on the changes with respect to C-GLORSv4. Section 3 presents selected results on the improvements borne by the latest version. Section 4 summarizes and discusses the main achievements. The Appendix presents in detail the reanalysis data product. Version 5 of C-GLORS will be updated in delayed time mode, typically with an approximate 1-year delay beyond present time due to the dissemination of quality-checked observational data.
In this section we briefly describe the reanalysis system, with the aim of highlighting the changes with respect to version 4. A detailed description of the reanalysis system for v4 is available in Storto et al. (2016a). Storto et al. (2014) also describe the choices for background-error covariances in C-GLORS.
The reanalysis spans the period 1979–2015, although data are released from
1980 onwards as the first year is affected by an initial adjustment. The
reanalysis system includes a (i) three-dimensional variational data
assimilation scheme called OceanVar (see next section) that
assimilates hydrographic profiles from the UK Met Office Hadley Centre EN3
dataset (ENACT/ENSEMBLES, version 3) (Ingleby and Huddleston, 2007) until
2012 and EN4 dataset (Good et al., 2013) afterwards, and along-track
altimetric observations provided by AVISO following Storto et al. (2011);
(ii) the NEMO ocean model (Madec, 2008), configured at about
C-GLORS is forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim atmospheric reanalysis (Dee et al., 2011) using the bulk formulas from Large and Yeager (2004). ERA-Interim provides 3 h fields of temperature and humidity at 2 m, wind at 10 m, daily mean fields of short- and long-wave radiation, and total and solid precipitation. The short-wave radiation is modulated through the scheme of Bernie et al. (2007) to reproduce its diurnal cycle. Storto et al. (2016a) show the positive contributions of all assimilation components (OceanVar, surface nudging, LSBC) to the performance of C-GLORS. More details on the data assimilation formulation are provided in the next section.
The data assimilation system used in C-GLORS is a three-dimensional
variational (3DVAR) scheme called OceanVar (Dobricic and Pinardi, 2008;
Storto et al., 2014). The assimilation time window is 7 days and the
frequency of the assimilation steps is also 7 days. The 3DVAR scheme
minimizes a cost function
The observation-error covariance matrix is assumed diagonal, i.e. observation errors are assumed independent between any pair of observations. For in situ observations, the error accounts for instrumental accuracy and representativeness error and is defined as a function of the parameter (temperature or salinity), type of observation (bathythermographs, CTDs, floats or moorings), the depth and the location. For sea level anomaly, the error variance accounts for the satellite instrumental accuracy, the mean dynamic topography error (provided by CLS/AVISO) and the representativeness error. Usual climatology and background quality checks are implemented in C-GLORS and described by Storto et al. (2016a).
We review here the main improvements of C-GLORSv5 with respect to its predecessor C-GLORSv4. The upgrade of the system follows the validation exercise performed with C-GLORSv4, which highlighted the need to solve some deficiencies present in that version. The completion of the new version has also been fostered by the requirements of many users in term of augmented output frequency and variables. Below, we detail the changes for each of the reanalysis components.
The NEMO version used for C-GLORSv5 is 3.2. No major differences are
implemented in this version of C-GLORS except for a tuning of the turbulent
kinetic energy (TKE) vertical mixing closure scheme. Following the work of
Calvert and Siddorn (2013) and Megann et al. (2014), values for the
background vertical viscosity and diffusivity were increased to
1.2
Additionally, C-GLORSv5 no longer includes the ERA-Interim forcing correction described in Storto et al. (2012, 2016a). This choice reflects the need to avoid specific post-processing of the atmospheric forcing, difficult to achieve within near real-time production, along with the idea of preferring the use of atmospheric parameters in balance with each other rather than introducing imbalances due to the correction of radiative fluxes only.
Observation-error profiles for use in the in situ data assimilation in C-GLORSv4 (black lines) and v5 (red lines), as a function of parameter and observation type.
The variational data assimilation system in C-GLORSv5 has been slightly modified compared to its predecessor's reanalysis and in particular both observational and background-error covariances were retuned, plus a few additional improvements and bug fixes.
The tuning of in situ observational errors was achieved by using data assimilation output statistics from C-GLORSv4 and applying the posterior method of Desroziers et al. (2005). Such a method relies on the cross-covariance statistics of observation misfits and assimilation residuals to diagnose background- and observation-errors in observation space. It is broadly used in meteorological and oceanographic applications because of its simplicity, although limits of the methods are found in the case of incorrectly prescribed spatial correlations or asynchronous tuning of background- and observation-error variances (Menard, 2016).
Figure 1 shows the profiles of observational errors for in situ observations before and after the tuning, for the four in situ observation types assimilated in the system. While the shapes of the vertical profiles are very close in C-GLORSv4 and v5, salinity errors have been approximately halved in the upper ocean for all observation types, implying that salinity analysis increments in C-GLORSv4 were underestimated, based on this posterior diagnostics. Differences between the two temperature error profiles depend on the type, while mooring (buoys) errors are reduced, Argo and CTD (conductivity temperature depth) errors are increased and expandable bathythermograph (XBT) errors remain almost unchanged. The tuning in practice, flattens the differences in errors between the observation types, leading to a closer profile for the four observing networks than in C-GLORSv4. This result can be interpreted with the dominant effect of representativeness error compared to the instrumental error in ocean observations, the former being reasonably more similar between different observing networks.
Background-error vertical covariances have also been tuned in C-GLORSv5. The
modification consisted in introducing a corrective coefficient, by means of
which covariances were modulated in time and space. The new vertical EOFs
operator
Figure 2 shows the new time-averaged background-error covariances after the application of the corrective coefficients for the temperature in the top 10 m of depth (top panel) and the difference with the corresponding standard deviations in C-GLORSv4 (bottom panel). The use of the corrective factor generally reduces the covariances at global scale, with a particularly large reduction in the tropical Pacific Ocean and within the North Pacific Gyre. As discussed in Storto et al. (2014, 2016a), the overestimation in the tropical Pacific Ocean was a known issue, attributable to the dataset from which EOFs are calculated, which is formed by monthly mean anomalies. Indeed, such an approach by construction confuses the ocean variability with the ocean error variance. These two were found to diverge especially in the tropical oceans, where errors were overestimated. Conversely, some regions dominated by strong mesoscale activity (Gulf Stream, Kuroshio, Antarctic Circumpolar Current) exhibit an increase in background-error variances, suggesting that the method of using monthly mean anomalies may underestimate error variances in these areas due to the coarse temporal resolution of the monthly anomalies with respect to the weekly assimilation frequency.
Season-averaged temperature background-error standard deviation in the top 10 m of depth in C-GLORSv5 (top panel), and difference with respect to the background errors used in C-GLORSv4 (bottom panel).
Figure 3 shows the temporal coefficient
C-GLORS implements a background quality check procedure that rejects
observations whose square departure exceeds a certain number of times the sum
of the observational and background-error variances. For the
with
The rejection of observations near the coast has also been softened, with a reduction in the minimum distance from 75 to 15 km from shorelines. Finally, a bug preventing the assimilation of observations located at a depth shallower than the middle of the first model level (around 0.5 m) was fixed in C-GLORSv5, allowing a greater number of sea surface observations from in situ profiles to be ingested. This change produced a 0.9 % increase in assimilated observations (peaking at about 3 % at the beginning of the 1980s when there were less profiles sampling the deep waters).
Background-error standard deviation temporal inflation (coefficient
As C-GLORSv5 is conceived to be continuously updated in delayed time mode, a change in the observational data was made during the production of the reanalysis. In particular, from 1979 to 2012 the in situ data were extracted from EN3 (Ingleby and Huddleston, 2007). From 2013, the reanalysis assimilates hydrographic profiles from EN4 (Good et al., 2013). A similar change concerns also altimetry data: two versions of the delayed time along-track dataset from CLS/AVISO with the upgrade occurring at the beginning of 2013. The mean dynamic topography (MDT) in C-GLORS is computed as long-term mean sea surface height (Storto e al., 2016a) from a twin experiment with the assimilation of in situ profiles only. Because the two versions of assimilated AVISO sea level anomalies (SLAs) are referenced to 1993–1999 and 1993–2012, respectively, at the beginning of 2013 the MDT was recalculated as 1993–2012 long-term mean from the previous 20 years of reanalysis and substituted to the 1993–1999 referenced MDT. This approach preserves the continuity of the MDT used and prevents from possible adjustments from 2013.
The large-scale bias correction (LSBC) scheme of C-GLORS relaxes the temperature and salinity towards large-scale univariate objective analyses of temperature and salinity measurements from hydrographic profiles. This scheme has proven successful for bias- and drift-correcting long-term diagnostics in C-GLORS, mostly arising from systematic errors in the forcing (Storto et al., 2016b). The new version of C-GLORS has a decreased temporal scale for use in this scheme, which changed from 3 to 36 months. Preliminary tests showed that a 3-month timescale was partly preventing the inter-annual variability of the heat content to evolve independently from the objective analyses, resulting in a too close reproduction of the latter. The new timescale shows robust in preventing large-scale biases without jeopardizing the reanalysis inter-annual variability. This is shown in Fig. 4, which reproduces the global ocean heat content in non-assimilative experiments covering the period 1958–1989 for different timescales of the large-scale bias correction. Without LSBC, the model drifts away from the initial conditions, due to weakly negative air-sea heat fluxes (Storto et al., 2016b). With a 3-month period, the variability of the modelled ocean heat content resamples very well that of EN4, while the 3-year timescale prevents the simulation to drift without following closely EN4.
Total ocean heat content in a set of preliminary experiments during 1958–1990 performed to test the impact of the large-scale bias correction (LSBC) timescale. Thin lines refer to monthly means and thick lines to yearly means. Black lines refer to the experiment without LSBC; red and orange lines to the experiment with LSBC and with a timescale of 3 months and 3 years, respectively. Also shown is the heat content computed from the UK Met Office EN4 dataset (Good et al., 2013).
Verification skill scores (bias and RMSE) of C-GLORSv4 and C-GLORSv5 against Argo floats from EN4 over the period 2005–2013 as a function of ocean depth. Units are psu and degrees Celsius for salinity and temperature, respectively. Bias is defined as observation minus model fields.
In C-GLORSv4, we assimilated sea-ice concentration analyses retrieved from the Defense Meteorological Satellite Program (DMSP) constellation of passive microwave radiometers, provided by the National Snow and Ice Data Center (NSIDC) through the use of the NASA team algorithm (Cavalieri et al., 1999). While C-GLORSv4 has a good representation of seasonal and inter-annual variations in sea-ice extent and area, the ice volume was characterized by anomalous variability, which we found was caused by the assimilation of sea-ice concentration in highly ice-covered areas, leading to spurious increases in ice thickness. This is a common problem when sea-ice concentration is assimilated but no constraint is applied to sea-ice thickness (e.g. Tietsche et al., 2013). In C-GLORSv5 we introduce a nudging scheme in order to weakly constrain sea-ice thickness in the Arctic Ocean. Model sea-ice thickness is relaxed towards PIOMAS data (Pan-Arctic Ice Ocean Modeling and Assimilation System; Zhang and Rothrock, 2003) with a 15-day relaxation timescale. Although PIOMAS is a reanalysis itself that assimilates only ice concentration data, it has been extensively validated against independent data and proves a reliable tool for the reconstruction of the Arctic sea-ice volume in the past few decades (Schweiger et al., 2011). This approach proves able to mitigate the spurious variability of sea-ice volume, although future work should be done to implement robust multivariate corrections of sea-ice parameters in polar regions.
As Fig. 5, but for the tropical ocean skill scores (between
30
The initialization strategy in C-CGLORSv5 was changed. While C-GLORSv4 was initialized through a spin-up with repeated atmospheric forcing relative to 1978 for 10 years, C-GLORSv5 was initialized using the 1979–1982 mean January conditions. This led to a weaker shock at the beginning of the reanalysis, allowing the dissemination of the reanalysis data from 1980.
In this section, we review the results that summarize the main improvements of C-GLORSv5 compared to its predecessor (C-GLORSv4).
Verification of model fields against Argo floats from EN4 was conducted for
both C-GLORSv4 and C-GLORSv5. Output fields are compared to the data before
the assimilation. Observations from floats represent a fairly independent
dataset, because their successive measurements are sampled at different
locations, thus the temporal correlation of the observational error can be
reasonably assumed to be negligible. We report in Fig. 5, global vertical
profiles of bias and root mean square error (RMSE) for salinity and
temperature. The validation results suggest that differences between the two
versions are not large. In particular, C-GLORSv5 is able to reduce the warm
temperature bias in the upper ocean, which leads to a corresponding RMSE
decrease in the top 80 m of depth. Below this depth, statistics show a cold
bias and a corresponding slight increase in RMSE, suggesting the need to
further retune the vertical mixing scheme. On the other hand, salinity skill
scores are characterized by a saline bias in the upper part of the ocean,
which slightly increase in C-GLORSv5, and it is due to the detrimental
contribution of the high latitudes (Fig. S1 in the Supplement shows the skill
scores for the global ocean between 60
As Fig. 5, but for the North Atlantic Ocean skill scores (between
20
The improvement of the upper ocean temperature is particularly evident in the tropical region. Figure 6 reproduces the same statistics as Fig. 5 but for the tropical oceans only, highlighting the significant reduction of bias for salinity in the top 50 m and the temperature below 50 m, although RMSE skill scores are not significantly affected. In the North Atlantic Ocean (Fig. 7), results show the improvement of salinity scores, again in the first 50 m, while the impact of the version upgrade is positive at depths around the mixed layer depth (40 to 50 m of depth) and negative below, up to 800 m of depth.
Figure 8 shows the sea surface temperature RMSE difference between C-GLORSv4
and v5 against NOAA sea surface temperature (SST)
Map of differences of SST Root Mean Square error between C-GLORSv4
and C-GLORSv5. The RMSE is computed against NOAA SST
A slight worsening occurs also at very high latitudes, although in these
areas NOAA SST data are mostly inferred from sea-ice concentration data
through empirical relationships (Reynolds et al., 2007) and therefore are
less reliable for validation purposes. The global improvement of SST in terms
of RMSE decrease is 3.2 %. It peaks at 7.7 % in the tropics (from
30
Among the main climate change signatures on the oceans, the ocean heat
content has increased notably during the last few decades (Levitus et al.,
2012). Related to it, the change in the global steric sea level represents a
fundamental diagnostics that ocean reanalyses need to capture, whereas the
global change in the halosteric component is rather neutral at global scale
(Stammer et al., 2013). Figure 9 shows the global steric sea level change
(calculated over the top 2000 m, for consistency with observational
datasets) in C-GLORSv4, C-GLORSv5, the NODC in situ based estimates (Levitus
et al., 2012), and the dataset proposed by Storto et al. (2016c) for the
period 2003–2011 merging satellite altimetry and gravimetry data. For the
latter, global steric sea level is inferred by the difference of global sea
level change from altimetric missions minus global barystatic sea level
change from the GRACE gravimetric mission. C-GLORv5 data start in 1980 unlike
C-GLORSv4 in 1982. The 1980–1982 years are characterized by an abrupt steric
sea level decrease following the eruption of El Chichón (Church et al.,
2005). Similarly, a decrease at the beginning of the 1990s follows the
Pinatubo eruption, whose effects appear to last longer in C-GLORSv5. A
significant difference between the two versions of C-GLORS occurs in the last
decade (from 2005 onwards), where C-GLORSv5 reduces the warming hiatus
(Trenberth and Fasullo, 2013) present in C-GLORSv4. This feature leads
C-GLORSv5 to have a 2003–2011 trend higher than C-GLORSv4 (0.57 vs.
0.02 mm year
Global ocean steric sea level (SSL) (calculated over the top 2000 m) for the reanalyses C-GLORSv4 and v5, along with estimates from satellite altimetry and gravimetry (ALT–GRV, Storto et al., 2016c) and NODC pentadal estimates (Levitus et al., 2012). The legend reports linear trends for the period 2003–2011 (for comparison with ALT–GRV), and 1983–2013 (for comparison with NODC SSL and the temporal correlation against the two validating datasets during their respective period. Correlations are statistically significant if greater than 0.25 and 0.46 at 99 % confidence level, based on bootstrapping, for the 2003–2011 monthly data and the 1983–2013 yearly data validation, respectively.
To further deepen the main changes of C-GLORSv5 with respect to v4 in terms of the last decade's heat content increase, Fig. 10 shows the 2005–2013 top 2000 m heat content trend in C-GLORSv5, the NOAA/NODC datasets and trend differences between C-GLORSv5 and v4. From this sketch, it appears that heat content trend is in close agreement with the NOAA/NODC estimates. In particular, the increase in linear trends in C-GLORSv5 with respect to its predecessor occurs mostly in the middle of the North Atlantic Gyre and, only partly, in the tropical Pacific Ocean. The heat content difference in the second half of the 2000s between the two reanalysis versions is mostly contributed by the North Atlantic Ocean, whose trends now closely resembles those estimated by the NOAA/NODC. This is in agreement with the SST RMSE reduction in the middle of the North Atlantic Gyre, previously shown, which contributes to drive the subsurface inter-annual variability shown here in terms of trends. This improvement is likely due to the weakening of the bias correction scheme in association with the decrease in background-error covariances in the last decade (Fig. 3), which jointly strengthen the weight given to temperature measurements from 2000 onwards and sustain the global signal.
We compare, in this section, the representation of the Atlantic Ocean
meridional overturning circulation (AMOC) estimated from C-GLORSv4 and v5
with the RAPID-MOC array estimates (Rayner et al., 2011). This is shown in
the top panel of Fig. 11 in terms of the maximum of the meridional
streamfunction at 26
Atlantic meridional overturning (AMOC, top panel, defined as maximum
of the meridional streamfunction at 26.5
More importantly, the long-term average of C-GLORSv5 is 1.3 Sv higher than
in C-GLORSv4, getting closer to the observational estimates and reducing the
underestimation of AMOC in the previous reanalysis, due to an underestimation
of the Florida Current and the interior southward transports (Storto et al.,
2016a). In particular, the largest effect of the new reanalysis configuration
involves the strengthening of the southward transport of the upper North
Atlantic Deep Water at 26
The increase of AMOC in C-GLORSv5 is more evident up to the beginning of the
2000s; afterwards, the two versions show very similar values, i.e. the mean
increase of AMOC is significant in data-poor periods, suggesting that it is
likely due to refinement of large-scale bias correction, atmospheric forcing,
model vertical physics or sea-ice treatment rather than the tuning of the
3DVAR scheme. However, the reanalyses fail in capturing the first two AMOC
peaks (2005 and 2006), due to the underestimation of the Florida Strait
contribution to the total AMOC (Fig. S2), likely caused by the lack of in
situ observations at that time along with the difficulty in resolving the
Florida Strait geometry with the
Antarctic (left panels) and Arctic (right panels) sea-ice area (solid lines) and volume (dashed lines) from C-GLORSv4 and v5. Top panels show yearly mean values and bottom panels show the 1982–2013 monthly climatology.
The meridional heat transport (MHT) at 26
Sea-ice areas and volumes from C-GLORSv5 are shown in Fig. 12 for both the Antarctic and Arctic oceans and compared to C-GLORSv4. Sea-ice areas between version 4 and 5 are in close agreement for both polar regions, as a consequence of the same sea-ice concentration data assimilation scheme and observational dataset. On the contrary, the sea-ice volume variability and trend significantly change between the two versions, especially in the Arctic region.
For the latter, the spurious variability present in version 4 – sea-ice
volume minima during 1987–1988 and maximum during 2006 – is replaced by a
more realistic and smaller inter-annual variability, with minima correctly
occurring during September 2007 and 2012. Furthermore, the sea-ice
accumulation in the Beaufort Gyre that affected C-GLORSv4 (not shown) is no
longer present in v5. This obviously improves the estimates of the autumn
(October–November) sea-ice volume trends, equal to
Similarly, sea-ice volume spurious variability, albeit smaller, is recovered
in the Antarctic region as well. This leads to better sea-ice volume trend
estimates, equal to
Based on this assessment, C-GLORSv5 may be used as a tool for investigating the ocean and sea-ice inter-annual variability in polar regions. For instance, Mayer et al. (2016) make extensive use of C-GLORSv5 data (ocean heat content, sea-ice concentration and thickness, sea-ice velocities) to investigate the Arctic region energy imbalance.
Zonally averaged net heat flux (downwards) climatology (1982–2013): C-GLORSv5 (left panel), difference between OAFlux and C-GLORSv4 (middle panel) and difference between OAFlux and C-GLORSv5 (right panel).
A possible application of ocean reanalyses is the study of heat budget (e.g. Yang et al., 2016a). Therefore, validation of air-sea heat fluxes represents a necessary step for this kind of application. C-GLORSv4 was affected by an underestimation of air-sea net heat flux (Valdivieso et al., 2016) due, among other factors, to the radiative flux corrections that were unbalanced with turbulent atmospheric forcing, inducing spuriously negative net heat fluxes at low- and mid-latitudes (Storto et al., 2016a).
The use of uncorrected forcing along with the tuning of the data assimilation system leads to reduced surface biases in C-GLORSv5, which translates to better air-sea heat fluxes. This feature is shown in Fig. 13, in terms of zonally averaged monthly climatology of net heat flux from C-GLORSv5, and differences between C-GLORSv4 and OAFlux (objectively analysed air-sea fluxes; Yu and Weller, 2007), and C-GLORSv5 and OAFlux. OAFlux is a satellite-based air-sea flux dataset that relies on the ISCCP (International Satellite Cloud Climatology Project, Zhang et al., 2007) for the net radiative fluxes at the sea surface. Despite the large uncertainty of net heat flux from observational estimates and reanalyses (Valdivieso et al., 2016), the use of this independent dataset for the reanalysis validation suggests that C-GLORSv5 has the net heat flux in the tropics (year-round) in closer agreement to OAFlux with respect to C-GLORSv4. Boreal winter fluxes in the Southern Ocean, underestimated in C-GLORSv4, also appear now in close agreement.
Improved heat flux estimates emerge also in the global net heat flux average.
While the previous C-GLORS reanalysis system was characterized by a large
negative imbalance (Valdivieso et al., 2016) equal to
We describe in this article the C-GLORSv5 global ocean reanalyses dataset,
available from
Following the lessons learned during the validation exercise of the previous version, this version of C-GLORS contains a number of significant improvements, among which a complete retuning of the data assimilation system (especially background- and observation-errors), retuned schemes for the large-scale bias correction and sea-ice data assimilation, as well as an updated version of input datasets and an improved initialization strategy. Additionally, a background-error temporal inflation coefficient has been introduced to mimic the reanalysis accuracy increase corresponding to the Argo floats deployment. The C-GLORSv5 reanalysis system also produced daily mean outputs available on demand, further to standard monthly mean outputs detailed in Appendix A.
While it is difficult to disentangle the individual effect of the reanalysis upgrades on the resulting reanalysis accuracy, we have focussed the validation exercise presented in Sect. 3 on highlighting the main changes and improvements of C-GLORSv5 with respect to its predecessor C-GLORSv4. Validation skill scores appear comparable between the two products, although C-GLORSv5 presents an improved representation of the upper ocean (top 80 m) temperature mean state and variability, particularly evident in the tropical region with a reduction of upper ocean warm biases, which in turn leads to an improved representation of the air-sea heat fluxes. For some skill scores, such as salinity in the upper ocean of polar regions and temperature below 100 m of depth, the changes in version 5 appears however slightly detrimental, requiring a further tuning of the model parameterizations (e.g. vertical mixing). Skill scores of SST against satellite data are also improved at global scale, although spots of increased RMSE exist, corresponding to areas of large variability, most likely due to the retuning of background-error covariances and the heavier weight attributed to in situ profiles.
The heat content change in the last decade also appears in closer agreement with independent estimates, likely due to the combined effect of the improved surface state and retuning of assimilation and bias-correction components of C-GLORSv5. This feature emerges in the North Atlantic Ocean. As a consequence, the representation of the Atlantic Ocean overturning circulation is also concerned with C-GLORSv5 showing a strengthening that brings it closer to the RAPID array estimates.
As no constraint was applied to sea-ice thickness in C-GLORSv4, the absence of sea-ice conservation methods – as the ones proposed, for instance, by Tietsche et al. (2013) – caused drifts in the sea-ice volume time series. An intermediate solution consisting in a weakly relaxation towards realistic sea-ice thickness data was implemented in C-GLORS for the Arctic region, which led to inter-annual variability for sea-ice volume in accordance to many model and observational studies. In future releases of C-GLORS, multivariate sea-ice assimilation will be considered, as it emerges as the desired strategy to simultaneously correct sea-ice parameters from sea-ice concentration data with multivariate balances, without the recourse to empirical nudging schemes for the surface state assimilation.
Overall, the reanalysis proves a valuable tool not only for regional downscaling and downstreaming applications, but also for process- and budget-oriented studies concerning, for instance, heat exchanges, air-sea fluxes and sea-ice energy. Additionally, climate-oriented ocean simulations (e.g. the ocean components of the Coupled Model Intercomparison Project, CMIP; e.g. Taylor et al., 2012; Griffies et al., 2016), may benefit from this dataset for comparison and validation purposes.
The according information can be found in Appendix A.
The following table describes the ocean and sea-ice parameters available from
C-GLORSv5 at
C-GLORSv5 disseminated ocean and sea-ice parameters.
interpolated onto a global regular grid at