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

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

FOAM model configurations 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) 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

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)

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

(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

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.

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.

(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

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

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

(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

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.

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 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

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

(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.

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

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 All observations binned together to get meaningful results Show different covariances with depth Large synoptic scales at 55m depth Depth = 800 m

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

(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

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

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