1. The impact of Argo data on ocean and climate forecasting
Matt Martin, Mike Bell
Contents:
1. Introduction
2. Data assimilation and Argo data
3. Indirect impact of Argo data
4. Summary
Mainly concentrate on results from Met Office FOAM operational system.
Will start off outlining the FOAM system including assimilation system
Then show examples of experiments showing the direct impact of Argo on the errors in the system
Then give some examples of how Argo data can be used to improve the assimilation/model system
Summary and future work

2. 1. FOAM system FOAM = Forecasting Ocean Assimilation Model
Real-time data
Obs QC & processing
Analysis
Forecast to T+120
NWP 6 hourly fluxes
Automatic verification
T+24 forecast used in QC
Product dissemination
Operational system
Hindcast system
FOAM – Forecasting Ocean Assimilation Model - runs every day in the operational suite at the Met Office producing analysis and 5 day forecasts of the deep ocean.
The data is obtained from the GTS (global telecommunications system), processed and quality controlled automatically
The data is used with the previous 24 hour forecast to produce an analysis
6 hourly fluxes are obtained from the Met Office NWP system and they force the model during the forecast
Also have ability to re-run the model/analysis in hindcast mode
Operational real-time deep-ocean forecasting system
Daily analyses and forecasts out to 5 days
Hindcast capability (back to 1997)
Relocatable high resolution nested model capability

3. FOAM model configurations
1° (operational since 1997)
1/9° (pre-operational since April 2002) Data available from
1/3° (operational since 2001)
Various resolution models
1 degree global model has been running since 1997
1/3 N atlantic model is nested inside global model and also runs operationally
Also run a 1/9 degree model with output freely available for research purposes on the web from Reading University website
All models have 20 levels in the vertical – will improve the vertical resolution soon

4. Operational data assimilation
Operational models assimilate:
Temperature profiles (including ARGO data)
In situ and satellite SST (2.5º AVHRR)
Satellite altimeter SSH (Jason-1, GFO, ERS-2)
SSMI-derived seaice data from CMC
Sequential scheme based upon the Analysis Correction scheme of Lorenc et al. (1991)
Operational upgrade implemented on 28th October 2003 includes:
Implementation of salinity assimilation
Significant developments to original system
Upgraded QC of data from ENACT project
Recently upgraded assimilation component of the system.
Previously only assimilated temperature data (not salinity) – will show the impact of assimilating Argo salinity on the system.
Only assimilated T data down to 1000m – will show the impact of assimilating at all depths
Will get higher resolution SST data, maybe from GHRSST project (part of GODAE)

5. 2. Data assimilation and Argo data
(a) Impact of salinity Argo data using simple assimilation scheme.
(b) Impact of Argo data in operational models.
(c) Comparison between impact of withholding Argo and other data types.
Show 3 different results from experiments which use Argo data.
Salinity assimilation
Impact on operational models
Withholding data experiments otherwise known as observation system experiments (OSEs).
One OSE from FOAM and one from ECMWF (European centre) seasonal forecasting system

6. (a) Investigation of impact of salinity data
Aim: To investigate the impact of the salinity data assimilation prior to implementation
5-month runs of the operational global 1º model
Running for Jan - May 2003
Forced by 6-hourly NWP surface fluxes
Initial state taken from operational model
Assimilating only Argo data - no other data types
Experiments run:
Assimilating temperature and salinity profiles
Assimilating temperature profiles only
Assimilating salinity profiles only
Control run assimilating no data
New assimilation scheme includes salinity assimilation so did some tests with the Argo data to assess its impact
Used global model for 5 months.
Only assimilated Argo data
Did 4 experiments with the different data combinations

7. Results (1)
RMS errors against observations that have not yet been assimilated for final month of integrations over entire globe
RMS T Error (ºC)
RMS S Error (PSU)
1.0
2.0
3.0
0.4
0.2
Depth (m)
200
400
600
800
No assim
S assim
T assim
T & S assim
Climatology
Temperature
Salinity
Red lines are without data assimilation
Yellow lines are with T & S assimilation
Green is climatology
Depth along horizontal axes
RMS errors against independent observations – obs which have not yet been assimilated
Assimilating S only degrades T, even worse than with no assimilation at all
Assimilating T only degrades S, “”
Probably because assimilating one and not the other degrades the density structure
Assimilating T&S improves salinity statistics significantly over assimilating S only.

8. Results (2)
Monthly mean salinity field differences (PSU) from Levitus climatology at 1000m for May (Levitus - model)
No assim
T assim
S assim
T & S assim
-1.0
1.0
0.0
Compare No assim with S assim – large biases disappear in the regions where there are data, except for in the N atlantic. Med outlow region especially – known to be wrong in 1 degree model – model bias could be feeding back onto the salinity.
With T & S assim, these biases in the N Atlantic are reduced.
South pacific still has large-scale biases because no Argo floats there.

9. (b) Preparation for operational implementation
Aim: To ensure that new operational system is working correctly and making better use of Argo data
Parallel suite trial running since August 2003
Upgraded version of operational FOAM suite
Global 1º and 1/3º North Atlantic models
Running daily at 05:00
Accessing only real-time data
Analysis and forecast cycle uses new assimilation scheme
Initial state from operational models
Comparing previous and current assimilation systems – making better use of Argo data in the new system
Running for about 3 months in parallel
Show results from 1/3 degree N Atlantic model
Only assimilated real-time data
Initial state for both runs was from old operational models

10. Impact of salinity data on operational models
Salinity data assimilated in upgraded system, but not in previous operational system
Salinity differences (PSU) from Levitus climatology on 15th September at 300m (Levitus - model):
New operational model
-1.0
0.0
1.0
In the old system, no salinity assimilation which led to large biases in salinity compared to climatology of order 1 psu. Example shown at 300m depth on rotated grid – point out landmarks.
New system assimilates salinity and the simple assimilation scheme significantly reduces the large biases in the North Atlantic.
Even after only about 1 and a half months of running.
Previous operational model

11. Impact of temperature data on operational models
Temperature data not assimilated below 1000m in current operational system
Temperature differences (ºC) from Levitus on 15th September at 1500m (Levitus - model):
In the old system, previously no T data assimilated below 1000m. Let to large drifts in T below this depth. Showing T differences from climatology at 1500m.
Biases seem to have been reduced even after only about 1 and a half months of model runs.
Previous operational model
New operational model
-2.5
0.0
2.5

12. (c) Data withholding experiments – impact of Argo and altimeter data
The reference integration assimilates all data
Other integrations withhold selected data types
Details of experiment:
1/9º North Atlantic model
integrations from Jan – Mar 2003
initial state from operational models
rms differences calculated using profile data before their assimilation
old assimilation scheme used
To assess the impact of Argo data and compare it with the impact of altimeter data in FOAM.
Use 1/9 degree N Atlantic model
3 month runs
Used old system so only look at impact on temperature

13. Impact of withholding Argo and altimeter data from FOAM
Differences between model and observations yet to be assimilated
FOAM 12km N Atlantic model driven by 6 hourly fluxes from Jan-Mar 2003
SST, XBT and Pirata data are also assimilated
old assimilation scheme
Show differences from independent profile observations for whole of 3 month period over whole of N Atlantic
Depth along horizontal axis
All data and altimeter data are similar – altimeter doesn’t have much impact on T – uses old assimilation scheme which is being improved
Withholding Argo data has large detrimental impact on the RMS errors – shows the value of the Argo data.

14. Impact of withholding different data types in seasonal forecasting
From ECMWF seasonal forecasting system (HOPE model, OI scheme)
Potential temperature RMS differences from experiment with all data assimilated,
Upper 300m of ocean
From A. Vidard
No moorings
No Argo
No XBT
5 year integrations from ECMWF – European Centre – provided by Arthur Vidard
3 runs withholding different types of temperature obsevations and looking at the RMS differences to a run which assimilated all data
Argo data only really available for the last couple of years of the integration so not fair to Argo but does show that Argo has a large impact on temperature analysis

15. 3. Indirect impact of Argo
(a) Improved estimation of error covariances.
(b) Mixed layer improvements.

16. (a) Model Error Covariances estimated using pairs of observed temperature profiles
Use collocated observation and model forecast values to estimate covariance values – bin together to have enough statistical information
Assume separability of the error covariance, i.e. horizontal and vertical correlations can be calculated separately.
Assume the forecast errors arise from two distinct sources:
errors in the internal model dynamics => “mesoscale” errors
errors in the atmospheric forcing => “synoptic” scale errors
Fit a combination of 2 SOAR functions to the (obs-f/c) covariance values to estimate the variance and horizontal correlation scales of the two forecast error components.
The observation error variance is the difference between the total (obs-f/c) mean square error and the total forecast error variance.

17. Modelling the covariance
Schematic of method
Example at 30W, 40N for SSH.
Mesoscale length scale = 37km
Synoptic length scale = 560km
Circles - (obs-forecast) covariances
Dotted line - synoptic scale function
Dashed line- mesoscale function
Solid line - sum of the two functions

18. Temperature profile error covariances
Variance Length
Meso km
Synop km
Variance Length
Meso km
Synop km
Covariance
Depth = 55 m
Depth = 240 m
Separation
Variance Length
Meso km
Synop km
All observations binned together to get meaningful results
Show different covariances with depth
Large synoptic scales at 55m depth
Depth = 800 m

19. Mesoscale error variances for SSH (cm²) and SST (K²) for 1/3o FOAM Atlantic model
SSH Variance
SST Variance

20. (b) 1D Mixed Layer Assessments using Argo data
Initialise T & S profiles using Argo observation
Use model and surface forcing to integrate forward for 10 days
Estimate error in forecast using next Argo observation
Run for 1 year for many different Argo floats
Use this framework to test assimilation strategy
Argo ob
Background
Forecast error statistics
Surface fluxes
Analysis
Depth
10 day forecast
Forecast
Argo ob

21. Assimilation in the mixed layer Timeliness of assimilation
MLD rms errors (m) 20 40 60
10 days
5 days
1 day
12 hours
6 hours
1 hour
No assimilation
“Kraus-Turner” scheme
“Large” scheme
For Large et al. model, the accuracy of the forecast decreases if the increments are nudged in over more than 1 hour
A large number of other factors can also be explored e.g. vertical resolution, time sampling of fluxes
Equatorial undercurrent now penetrates to the eastern Pacific and surface currents are better

22. Summary and future work
Argo salinity data assimilation improves salinity fields, even with simple scheme
Further work to improve salinity assimilation, i.e. use isopycnal coordinates for analysis
Both Argo and altimeter data assimilation improve the fit of the analyses to independent temperature data with Argo data having the largest impact in FOAM
Argo data also has significant usage for improving the data assimilation methods used, i.e. error covariances
Use Argo data to help improve the assimilation of altimeter data
Compare results in FOAM with other centres to make the most of the Argo data