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GOVST III, Paris Nov 2011 ECMWF ECMWF Report Magdalena Alonso Balmaseda Kristian Mogensen Operational Implementation of NEMO/NEMOVAR ORAS4: Ocean ReAnalysis System 4 Some lessons learnt during preparation Evaluation process Plans

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GOVST III, Paris Nov 2011 ECMWF Delayed Ocean Re-Analysis ~ORAS4 (NEMOVAR) Real Time Ocean Analysis ECMWF: Forecasting Systems ECMWF: Forecasting Systems Medium-Range (10-day) Partial coupling Medium-Range (10-day) Partial coupling Seasonal Forecasts Fully coupled Seasonal Forecasts Fully coupled Extended + Monthly Fully coupled Extended + Monthly Fully coupled Ocean model Atmospheric model Wave model Atmospheric model Ocean model Wave model Ocean Initial Conditions

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GOVST III, Paris Nov 2011 ECMWF ECMWF has a implemented new operational ocean re-analysis system. http://www.ecmwf.int/products/forecasts/d/charts/oras4/reanalysis/ It implies the transition to NEMO/NEMOVAR from HOPE/OI It consists of 5 ensemble members, covering the period 1958-Present, continuously updated. It is used for the initialization of the operational monthly and seasonal forecasts. It is also used to initialize the CMIP5 decadal forecasts (EC-Earth …) It is a valuable resource for climate variability studies. Documentation in preparation: Mogensen et al 2011, Balmaseda et al 2011 ECMWF: Operational Ocean Changes in 2011. ORAS4

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GOVST III, Paris Nov 2011 ECMWF Ocean Model: NEMO V3.0 ORCA1 and 42 levels (ocean) Data Assimilation: NEMOVAR (3D-var FGAT). Data: Temperature and Salinity Profiles (EN3-XBT corrected and GTS), SST (HADISST/ OIv21x1 /OSTIA), along track Altimeter Sea Level (AVISO). See figure below Forcing: ERA40/ERA-INTERIM/ECMWF NWP (see figure below) Bias Correction: In T/S and P gradient. Seasonal prescribed (from Argo+Alti) + Adaptive on line Ensemble Generation: wind perturbations, observation coverage, spin-up. 5 ensemble members ORAS4 Main Ingredients

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GOVST III, Paris Nov 2011 ECMWF What have we learned in the preparation process? Which SST product to use? Which products are available? Criteria for evaluation Assimilation of altimeter: variational implementation of Cooper and Haines in 3Dvar. Non trivial. Sorted. Coastal Covariances: Impact of Assimilation in the Atlantic MOC. Bias correction scheme: estimation of the offline term from Argo period. (Not a problem, a success; It affects the results) How to evaluate ocean reanalyses?

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GOVST III, Paris Nov 2011 ECMWF Which SST product to use? Options for Re-analysis OIV2_1x1: (weekly)~1982 onwards OIV2_025_AVHRR(daily)~1982 onwards OIV2_O25_AVHR+AMSR:~2002 onwards Options for Real-Time: As before + OSTIA (from 2008 onwards): Consistency with atmospheric analysis OIV2_025_AVHRR: bias cold in the global mean (regional differences) Bias decreases with time. OSTIA in beween (not shown) Weaker interannual variability Fit to insitu Temperature: bias cold in tropics, better in mid latitudes,. Not clear impact on Seasonal Forecasts DECISION: OIV2_1x1 until 2010, OSTIA thereafter.

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GOVST III, Paris Nov 2011 ECMWF Assimilating Altimeter Data Assimilation of sea level anomalies: along track (new) 1.SuperObbing: rms of superobs used to account for representativeness error 2.Remove global sea level prior to assimilation 3.Multivariate relationship: How to project sea level into the subsurface T and S (next) Assimilation of Global Sea Level Trends (from gridded maps) Global sea level is assimilated: FWF=SL_trend obs -SH_trend model Choice of MDT (Mean Dynamic Topography) External Product: Rio9 Tried, but not good results, due to the mismatch between model and Rio9 It needs more work to have an “observation” bias correction For ORAS4: MDT from an assimilation run using T and S Balance relationship between sea level and T/S is a linear formulation of the Cooper and Haines scheme, taking into account the stratification of the water column

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GOVST III, Paris Nov 2011 ECMWF Multivariate balance for Altimeter IN NEMOVAR the balance is between sea level and steric height Original formulation of NEMOVAR α ref and β ref are calculated by linearizing the equation of estate using the background T/S values as reference. Comments: i) zref=1500m is arbitrary. An attempt to take into account that baroclinicity is low below this level. Can we account for the vertical stratification more universally? ii) this can lead to increments in model levels with large dz

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GOVST III, Paris Nov 2011 ECMWF But Impact on Steric Height not realistic: This problem not so apparent if assimilating T/S and altimeter, but it is still there. Why? Single Obs Experiments: T increment The temperature increment is applied to the thickest model levels

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GOVST III, Paris Nov 2011 ECMWF Modifications A):Weighting based on stratification. Use BV frequency to calculate α N and β N instead of equation of state B) Do not double-count balance-salinity corrections

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GOVST III, Paris Nov 2011 ECMWF New Balance formulation: Sea Level Altimeter (AVISO) CONTROL ASSIM: TS CONTROL+ALTI ASSIM: TS + ALTI Problem with Steric Height Solved Problem with deep T increments Solved Old New

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GOVST III, Paris Nov 2011 ECMWF CONTROL ASSIM: T+S ASSIM: T+S+Alti EQ Central Pacific EQ Indian Ocean TROPICAL PacificGLOBAL Altimeter Improves the fit to InSitu Temperature Data RMSE of 10 days forecast

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GOVST III, Paris Nov 2011 ECMWF Assessment of the ORA-S4 re-analysis Choose a baseline: the CONTROL (e.i., no data assim) 1.Assim Intrinsic Metrics Fit to obs (first-guess minus obs): Bias, RMS Error growth (An-obs versus FG-obs) Consistency: Prescribed/Diagnosed B and R This is insufficient to assess a Reanalysis product 2.Spatial/temporal consistency: long time series and spatial maps Time correlation with Mooring currents Correlation with altimeter/Oscar currents Transports (MOC and RAPID): short time series Quite limited records. Not always independent data 3.Skill of Seasonal Forecasts Expensive. Model error can be a problem. 4.Observing System Experiments

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GOVST III, Paris Nov 2011 ECMWF Assimilation Statistics: Incremental Analysis Update

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GOVST III, Paris Nov 2011 ECMWF Fit To Obs Bias thin lines RMSE thick lines Assimilation improves over the control everywhere. A large part of the improvements comes from the reduction of bias. Note large errors in Extratropics come from WBC and coastal areas, where obs are given little weight

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GOVST III, Paris Nov 2011 ECMWF Fit to Obs ORAS4 shows reduced RMSE and bias respect the CNTL, in both T and S The bias is ORAS4 is more stable in time Fit improves with time, both ORAS4 and CNTL :Not only more subsurface obs, but better surface forcing and SST data?.

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GOVST III, Paris Nov 2011 ECMWF Fit to ADCP mooring data Some improvement of the Pacific and Atlantic undercurrents, which are still on the weak side.

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GOVST III, Paris Nov 2011 ECMWF NEMOVAR re-an: verif. against altimeter data NEMOVAR T+S ORA-S4: NEMOVAR T+S+Alti CNTL NEMO NoObs

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GOVST III, Paris Nov 2011 ECMWF Comparison with RAPID derived transports Atlantic MOC at 26N Short time series ORAS4 underestimates the MOC Note the large minima in 2010 and 2011!!

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GOVST III, Paris Nov 2011 ECMWF More MOC diagnostics RAPID ORAS4 CNTL In low res model the Florida Strait transport is not so well defined. Assimilation reduction of the FST is proportional to the weight is given to the obs (not shown) Ocean model tends to produce too strong and shallow AABW cell MOC profile

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GOVST III, Paris Nov 2011 ECMWF Impact on Of ORAS4 in SST Seasonal Forecasts Anomaly correlation: ORAS4 CNTL Persistence

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GOVST III, Paris Nov 2011 ECMWF El Chichon Pinatubo Global Surface Heat Fluxes from Reanalysis

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GOVST III, Paris Nov 2011 ECMWF Mean and time variability of ORAS4 oceanic heat transport. (GW2000: Ganachaud and Wunsch 2000) ERA+ASSIM heat flux integral ORAS4 total (whole depth) heat content The time integral of the ERA+ASM surface heat flux results in the evolution of the total ocean heat content Climate Applications

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GOVST III, Paris Nov 2011 ECMWF Summary Operational implementation of NEMO/NEMOVAR in forecasting systems and ocean reanalysis Transition from HOPE/OI ORAS4: new Ocean Reanalysis with NEMOVAR Still climate resolution: approx 1x1 degree. Some lessons learns in the preparation process Choice of SST product for reanalysis not trivial. Next is to try OSTIA reanalysis Balance relationship between altimeter and T/S not solved problem. Not trivial. Still room for improvement Improved covariance needed for the assimilation of observations near the coast. How to evaluate an ocean assimilation system and ocean reanalyses product? Evaluation of the ocean reanalysis is a pre-requisite for the interpretation of climate signals Standard assimilation statistics needed but not sufficient for the reanalyses Need information about time variability: sustained time series are very important: (altimeter, moorings, RAPID, other?) Impact on seasonal forecast is a test and a result. What is next? Document system, web pages, papers Higher resolution ocean model and reanalysis(ORCA 025) Sea-Ice model in monthly, seasonal forecast and reanalyses Improved coupling (bulk formula, wave effects, ocean mixed layer) Increased coupling (forecast and analysis)

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