Contents

1. The use of Argo data in the operational ocean forecasting activities at the UK Met OfficeMatt Martin Bruce Ingleby, Doug Smith

2. Contents • Uses of Argo data at the Met Office • Short-range operational ocean forecasting (FOAM) • Seasonal forecasting (GloSea) • Decadal prediction (DePreSys) • Objective analyses (GloSea and HadGOA) • Model development • Timeliness of Argo data • Assessment of the quality of the Argo data • Suitability of Argo data coverage • Impact of Argo data on analysis/forecast skill • Other potential uses of proposed Argo developments

3. Uses of Argo dataForecasting Ocean Assimilation Model (FOAM) 1º Global 1/3º N. Atlantic and Arctic 1/9º North Atlantic • Current operational version uses the Unified Model (UM) ocean model which is based on the ocean model used for climate modelling at the Hadley Centre. • Various nested configurations are run daily in the Met Office operational suite, forced by 6 hourly surface fluxes from NWP. They produce analyses and 5-day forecasts. • Uses Optimal Interpolation type scheme to assimilate: • in situ and satellite SST • satellite altimeter SSH (using Cooper and Haines scheme) • temperature and salinity profile data (including Argo data, XBTs, TAO/PIRATA,…) available over the GTS. • sea-ice concentration. • Data quality control system described later. • Data also used for validation/verification operationally. Martin, M.J., A. Hines and M.J. Bell, 2007. Data assimilation in the FOAM operational short-range ocean forecasting system: a description of the scheme and its impact. Q. J. R. Meteorol. Soc., accepted.

4. Uses of Argo dataGloSea seasonal forecasting system • Model • Enhanced version of the Hadley Centre climate model HadCM3. • 41 member coupled ocean-atmosphere global forecast ensemble. • Real-time system, run to 6 months ahead from 1st day of each month. • Analysis • 5 ocean analyses from perturbed wind stresses • Ocean analyses further perturbed with instantaneous SST perturbations • Assimilates the in situ profile data (including Argo data) using the same scheme as FOAM. This data is obtained from the GTS. Graham, R. J., M. Gordon, P. J. McLean, S. Ineson, M. R. Huddleston, M. K. Davey, A. Brookshaw, and R. T. H. Barnes, 2005. A performance comparison of coupled and uncoupled versions of the Met Office seasonal prediction general circulation model. Tellus, 57(3):320-339.

5. Uses of Argo dataDecadal Prediction System (DePreSys) Model • Uses the same coupled model as the Hadley Centre model (HadCM3). • Include changes in greenhouse gases and sulphate aerosols (SRES B2 scenario – intermediate changes). • Repeat previous 11-year solar cycle in forecasts. • Decay volcanic aerosol from the start of a forecast. • Initialisation • Atmospheric winds, temperature and surface pressure from ERA40. • The ocean component is initialised with multivariate OI analyses of surface and sub-surface observations of temperature and salinity, including Argo. • Uses covariances computed directly from long integrations of the coupled model. • Data is assimilated as anomalies to avoid model drift. Anomaly correlation of SST at (130˚W,0˚N) Smith, D. M. and J. M. Murphy, 2007: An objective ocean temperature and salinity analysis using covariances from a global climate model. J. Geophys. Res., in press.

6. Uses of Argo dataDelayed-mode quality control • A system for the quality control of profile data was developed for the EU ENACT and ENSEMBLES projects by Bruce Ingleby: • Includes a comprehensive set of checks: • track checks, spike checks, background check, stability checks, duplicate checks and buddy checks. • If half of levels are rejected then whole report is rejected. • If temperature is rejected then salinity also rejected. • Various versions of a comprehensive data-set containing the historical profile data, including Argo data, have been released and are available from http://hadobs.metoffice.com/en3/ • The data used were from various sources, including the Argo GDACs, GTSPP and WOD05. Ingleby, B. and M. Huddleston, 2007. Quality control of ocean temperature and salinity profiles — Historical and real-time data. J. Mar. Sys., in press.

7. Uses of Argo dataObjective analyses GloSea analysis • A modified version of the GloSea analysis procedure has been used to provide a multi-decadal objective analysis using the ENSEMBLES data. • These analyses have been used to assess the changes in ocean heat content over the past few decades. • They have also been useful in comparing the impact of various data types on the heat content estimates, which has revealed some significant biases between different data types. Hadley Centre Global Ocean Analysis (HadGOA) • Monthly analyses are calculated on isotherms which has potential advantages for calculation of heat content over analyses produced on depth levels • Evaluation of historical heat content variability • For use in climate model validation – analysis will be independent of GCMs. • Uses same data as GloSea analysis.

8. Uses of Argo dataModel development: mixed layer model tuning RMS errors in mixed-layer depth as a function of 2 model parameters • Example of FOAM model developments made using Argo data. • 1-D mixed layer models, forced by 6 hourly NWP fluxes, are compared with the Argo data over an annual cycle. • Statistics of mean and RMS errors are calculated over all floats. • Cheap to run, so various model parameters can be tuned to reduce RMS errors. Acreman, D. M., and C. J. Jeffery, 2007. The use of Argo for validation and tuning of mixed layer models. Submitted to Ocean Modelling.

9. Timeliness of Argo data FOAM perspective • FOAM runs daily at 0500UTC. • Produces an analysis which is valid at midnight the previous day. • Data which is not available on the day it is valid is given less weight in the analysis. • ~90% reported within 24 hours of their validity time and so would have been given full weight in FOAM. • Less than 1% are not assimilated at all. Difference between validity and receipt times (days) for all Argo reports over the GTS in January 2007.

10. Timeliness of Argo dataOther systems’ perspective GloSea • runs 10 days behind real-time, once per month. • uses data available over the GTS. • will assimilate over 99% of the Argo data (given table on previous slide). • in the near future, GloSea will change to run 6 days behind real-time. HadGOA • Not currently operational, but when it is will run about 10 days behind real-time, once per month. • Current timeliness suitable. DePreSys • Runs a month or two behind real-time. • Current timeliness suitable.

11. Quality of Argo data FOAM perspective • FOAM uses the automatic quality control system developed for ENACT • Less than 1% of floats are rejected. • Issues with bad data getting through: • previously, bad salinity data has got through the QC and has been assimilated into the model. • once assimilated, data affects other model fields through the model dynamics. • mainly a problem in areas of high natural variability where background check is less stringent. Percentage of Argo floats rejected in the FOAM automatic quality control in Jan 2007. • Blacklist: facility to blacklist data is available in FOAM but is rarely used. • It would be useful to be able to update the blacklist on a monthly basis based on the Argo grey list.

12. Quality of Argo dataGloSea perspective • As part of the GloSea quality control and objective analysis procedure, Bruce Ingleby has had a detailed look at Argo quality. • ~5% XBT failed automatic QC. Less for Argo ~1 or 2%. • For those that passed or partly passed, differences in average top 300m temperature have been used to identify bad data. • More “bad” Argo data than expected, mainly due to large data volumes. • Currently looking in even more detail at float statistics.

13. Quality of Argo dataGloSea perspective • “Frozen” profiles • Found issues with frozen profiles where identical profiles reported month after month. This can be spotted relatively easily by eye but not so easy in an automated system, particularly in real-time. About 40-50 floats identified with this problem. • Biases • Salinity biases well known. • Some temperature biases have been noticed. Large biases easy to detect, particularly in regions of low variability and at lower levels in the profile. In variable regions it can be difficult to decide if there’s a measurement bias or not. • Grey list • Argo grey list useful but incomplete. A significant number not on grey list (between 1/3 and 1/2).

14. Suitability of Argo data coverage • Horizontal • Altimeter SSH data used to resolve the mesoscale features (but doesn’t change the water-mass properties). • Surface forcing used to predict the small time and spatial scales of the mixed layer. • Both of these aspects require accurate specification of the large scale density structure which is available from Argo. • Scales in assimilation are 40km and 400km so a 3˚×3 ˚ array should be sufficient coverage for the large scales (but can always make use of higher resolution). • Vertical • Vertical resolution of Argo generally finer than model grid. However, would be good to have higher resolution near the surface. • Vertical extent (2000m) adequate. Require further studies (OSSEs) to provide more guidance on the suitability of the array design.

15. Impact of Argo dataFOAM Root mean square errors vs. in situ observations Orange - run without Argo data assimilated Black - run with all data assimilated • 5 year (Jan 2001 – Jan 2006) experiments run with nested configurations – results shown from 1/9˚ north Atlantic model. • Assimilated all available temperature and salinity profile data, plus SST data. • Re-ran without assimilating the Argo data. • Temperature errors are up to 40% larger without Argo. • Salinity errors over twice as large near the surface. RMS temperature error (˚C) RMS salinity error (psu/1000)

16. Impact of Argo dataFOAM Average (5 year) salinity field at 1000m difference from Levitus climatology (psu/1000) No Argo data assimilated All data assimilated • Large biases in the modelled salinity when Argo data is not assimilated (found to be due to vertical advection scheme). • Assimilating Argo salinity datacontrols the model drift to a large extent.

17. Impact of Argo dataGloSea • 6 ocean analyses were generated using GloSea. • 6 month hindcasts were run starting from each of these analyses. Each hindcast consisted of an ensemble of 15 members (generated through SST perturbations). • Start dates for the hindcasts were 1 February, 1 May, 1 August, 1 November for 2000-2004 Provided by Bruce Ingleby and Matt Huddleston

18. Impact of Argo dataGloSea Forecasts of 360m average temperature (~heat content) - anomaly correlations Provided by Bruce Ingleby and Matt Huddleston

19. Impact of Argo dataDePreSys Global annual mean surface temperature (TS) Global annual mean ocean heat content in upper 113m (H) • Hindcasts started from 1st March, June, September and December in each year from 1982 to 2001 (20 years x 4 seasons = 80 start dates). • Each hindcast is 10 years long. • Sample uncertainty in initial conditions with 4 ensemble members, starting from consecutive days. • Improved skill is found in hindcasts of global mean surface temperature. • This is explained mainly by ENSO in first year, and by better predictions of upper ocean heat content at longer lead times Provided by Doug Smith

20. Impact of Argo dataDePreSys Impact of ARGO data Explained variance is calculated as 100(1-E), where E is the expected OI analysis error (normalised by the climatological variance). Global mean monthly values shown have been smoothed with a 12 month running mean. Because of errors in the model covariances, E has been empirically adjusted based on results from a validation study. Solid=surface, dashed=300m Provided by Doug Smith

21. Other potential uses of proposed Argo developments • Within the FOAM group, a coupled physical-biological model (FOAM-HadOCC) has been set up with assimilation of both physical and biological variables (currently chlorophyll from SeaWiFS). • Oxygen data would be useful for validation and possibly for assimilation. • The Ocean Sea Surface Temperature and Sea Ice Analysis (OSTIA) system produces a 1/20˚ global daily SST analysis and is run operationally at the Met Office. • It would be useful for verification of the system to have independent temperature data close to the ocean surface. This data could also be used to improve the analysis.

22. Summary • Argo data are used in short-range ocean forecasting (FOAM), seasonal forecasting (GloSea), decadal prediction (DePreSys) and objective analyses (GloSea and HadGOA). • Timeliness is good although FOAM would make most of the data if it was available on the GTS within 24 hours of its validity time. • Number of floats failing quality control is small (~1%). Some issues remain with the automatic quality control of the data (eg “frozen” profiles). • Would be useful to be able to update a blacklist on a monthly basis using the central Argo grey list. • Argo data coverage is adequate for current applications. Would be useful to run experiments to examine the impact of data density on the accuracy of the forecasts. • Argo shown to have significant positive impact on all systems.