The impact of Argo data on ocean and climate forecasting

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

2. Operational system Hindcast system NWP 6 hourly fluxes T+24 forecast used in QC Obs QC & processing Analysis Forecast to T+120 Automatic verification Real-time data Product dissemination 1. FOAM system FOAM = Forecasting Ocean Assimilation Model • 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. 1° (operational since 1997) 1/9° (pre-operational since April 2002) Data available from http://www.nerc-essc.ac.uk 1/3° (operational since 2001) FOAM model configurations

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

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.

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

7. 3.0 0.4 2.0 0.2 RMS T Error (ºC) RMS S Error (PSU) 1.0 200 200 400 400 600 600 800 800 Depth (m) Depth (m) T assim T & S assim Climatology No assim S assim Results (1) • RMS errors against observations that have not yet been assimilated for final month of integrations over entire globe Temperature Salinity

8. No assim T assim T & S assim S assim -1.0 0.0 1.0 Results (2) • Monthly mean salinity field differences (PSU) from Levitus climatology at 1000m for May (Levitus - model)

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

10. New operational model 0.0 1.0 -1.0 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): 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): Previous operational model New operational model 0.0 2.5 -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

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

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, 1998-2003 Upper 300m of ocean From A. Vidard No moorings No Argo NoXBT

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

16. (a) Model Error Covariances estimatedusing 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 1.80 47 km Synop 0.5 1060 km Variance Length Meso 0.9 59 km Synop 0.1 540 km Covariance Depth = 55 m Depth = 240 m Separation Variance Length Meso 0.6 40 km Synop 0.2 500 km Depth = 800 m

19. Mesoscale error variances for SSH (cm²) and SST (K²) for 1/3o FOAM Atlantic model SST Variance SSH 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. 60 40 20 0 10 days 1 day 6 hours 0 No assimilation 5 days 12 hours 1 hour Assimilation in the mixed layerTimeliness of assimilation MLD rms errors (m) “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

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