Ongoing developments at ECMWF

EUROBRISA WORKSHOP, Dartmoor 21-24 July 2009 , ECMWF developments 1

Ongoing developments at ECMWF

Magdalena A. Balmaseda

ECMWF, Reading, U.K.


Overview

• Progress in Seasonal Forecasting in past 10 years End to End Seasonal Forecasting System Progress in ENSO Prediction: model and initialization To do:

o Can we measure progress on precipitation due to calibration….?o Can users give feedback to the observation community? Which are the needs?

• Ongoing developments at ECMWF NEMO/NEMOVAR: big investment in infrastructure. ERA-Interim: impact on Atlantic SST. Look at precipitation? Exploring limits on forecast horizons: 1yr and beyond Atmospheric model: Convection, WWB, resolution. Time line for implementation

• La Nina and impacts. Time for a Review focused on SA?

• Calibration: Some ideas on what to do next

• Summary


Pro

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End to End

Forecasting System

atmosDA

atmos obs

SST analysis

oceanDA

ocean obs

ocean reanalysis

atmos reanalysis

land,snow…?

sea-ice?

initialconditions

initialconditions

AGCM

OGCM

ensemble

generation

InitializationGCM integration

Ensemble Gen FC calibration


Evolution of the ECMWF SF

•Steady progress: ~1 month/decade skill gain

•Dramatic change in coupled behaviour between S1 & S2: bias and variability

•Improvement in S3, but still

• Warm(est) bias in eastern Pacific

•Underestimation of interannual variability

S1 S2 S3


Contribution of Initialization and Model

Relative Reduction in SST Forecast ErrorECMWF Seasonal Forecasting Systems

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S1 to S2 TOTAL OC INI MODEL



•For the prediction of ENSO-SST, it is possible to measure progress and to attribute improvements.

•Is it possible with other variables? Should it be tried with the EUROBRISA System?

•Is it possible to measure the impact of model/initialization and calibration?

•In particular, it is important to determine the relevance of the calibration period.

•It is important to give feedback to the wider community about the observational/reanalysis needs of forecast calibration.


Impact of observations (ocean and atmos)

Impact on 1-7 month SF of SST


Example from the Medium Range

Impact of Increased ensemble size versus longer calibration period

(Continuous Rank Probability Skill Score, T-2m Europe)

The longer calibration period has larger impact than increasing the ensemble size. From Hagerdorn 2008


On going developments at ECMWF

Preparations for S4


•COUPLED MODEL (IFS + OASIS2 + HOPE)•Recent cycle of atmospheric model (Cy31R1)•Atmospheric resolution TL159 and 62 levels•Time varying greenhouse gasses.•Includes ocean currents in wave model

•INITIALIZATION•Includes bias correction in ocean assimilation.•Includes assimilation of salinity and altimeter data. •ERA-40 data used to initialize ocean and atmosphere in hindcasts•Ocean reanalysis back to 1959, using ENACT/ENSEMBLES ocean data

•ENSEMBLE GENERATION•Extended range of back integrations: 11 members, 1981-2005.•Revised wind and SST perturbations. •Use EPS Singular Vector perturbations in atmospheric initial conditions.

•Forecasts extended to 7 months (to 13 months 4x per year).

The seasonal forecast System-3 (implem. March 07)


•COUPLED MODEL (IFS + OASIS3+ NEMO (ORCA1))•Recent cycle of atmospheric model (Cy35R3 and beyond)•Atmospheric resolution TL159 (TL255?) and 62-(90?) levels•Time varying greenhouse gasses.

•INITIALIZATION with NEMOVAR•Includes bias correction in ocean assimilation.•Includes assimilation of salinity and altimeter data. •ERA-40/ ERA-Interim data used to initialize ocean and atmosphere in hindcasts•Ocean reanalysis back to 1959, using EN3-XBt corrected ocean data

•ENSEMBLE GENERATION•Wind, SST and Freshwater perturbations. •Perturbations to the sea-ice concentration during forecast.

•Forecasts extended to 7 months (to 13 months 4x per year).

The seasonal forecast System-4 (2010?)


Impact of ERA-INTERIM

• Era Interim is from 1989, continuously updated about 2 months behind real time

• More up-to-date atmospheric model, increased resolution (from T159 in ERA-40 to T255), variational bias correction, 4D-var… See Uppala et al 2008, ECMWF Newsletter 115)

• Large impact on hydrological cycle, winds, solar radiation…

• Impact in the ocean is noticeable. It also affects the seasonal forecast skill

• Should be used for verification (shortcoming: does not go back for a long enough period).


ERA-40 versus ERA-InterimCorrelation with Altimeter data

correl

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Total Flux Correction

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Seasonal Flux Correction

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Interannual Flux Correction

0 5 10 15 20EastPac

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SsTrAtlEra-Interim improves the interannual variability of the ocean initial conditions, especially in the Equatorial and South-Tropical Atlantic


ERA-40 versus ERA-Interim: Forecast Skill

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wrt NCEP adjusted OIv2 1971-2000 climatology

EQATL SST anomaly correlation


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Ensemble sizes are 5 (esj6) and 5 (f1v1) 71 start dates from 19900101 to 20061001

EQATL SST rms errors

Fc esj6/m0 Fc f1v1/m0 Persistence Ensemble sd

MAGICS 6.11 verhandi - neh Mon Jun 29 19:07:57 2009

ERA-40ERA-Interim

Ocean Initial conditions prepared with ERA-Interim fluxes improve the forecast skill in the Equatorial Atlantic.


Onset of ENSO and MJO:

All the SF systems failed to predict the amplification of El Nino 1997 from spring starts (April/May 1997).

The reason: failure to generate a powerful WWB associated to an MJO event (already present in the initial conditions at the start of the integrations).


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Plot resolution is 2.8125 in x and 12 in yTime-longitude plot at 0.00 deg NSea level contoured every 0.05 mAnalysis

No interpolation

I.C. 19970501

-0.6-0.55-0.5-0.45-0.4-0.35-0.3-0.25-0.2-0.15-0.1-0.050.050.10.150.20.250.30.350.40.450.50.550.6

MAGICS 6.2 tamlane - neh Tue Apr 2 19:16:24 2002

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Plot resolution is 2.8125 in x and 12 in yTime-longitude plot at 0.00 deg N and 10.0 metres depthPotential temperature contoured every 0.5 deg CAnalysis

No interpolation

I.C. 19970501

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Analysis (hovmollers May-July 1997)

SST anom (c.i. 0.5 deg)

Sea Level anom (c.i. 5cm)Taux anom (c.i. 0.02 N/m2)

1st May 1997

1st May 1997

1st May 1997

•WWB in July associated to an MJO event (alrady present at initial time) reach peak values ~0.2N/m2 around dateline.

•They trigger a downwelling Kelvin wave. Peak values of SL anomalies in the Eastern Pacific reach 25 cm by mid June..

•SST anomalies reach maximum values of 4 deg in the Eastern Pacific by end of June-beg July

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Interpolated in y

I.C. 19970501

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-0.24-0.22-0.2-0.18-0.16-0.14-0.12-0.1-0.08-0.06-0.04-0.020.020.040.060.080.10.120.140.160.180.20.220.24



Coupled FC (hovmollers May-July 1997) (S3)

SST anom (c.i. 0.5 deg)

Sea Level anom (c.i. 5cm)Taux anom (c.i. 0.02 N/m2)

1st May 1997

1st May 1997

1st May 1997

•In the Coupled forecasts the surface signature of the MJO dies after 20 days, there is not any propagation to the Pacific, and there is not any WWB.

•As a consequence, the SL and SST anomalies of the coupled forecasts are those associated with the ocean initial conditions.

•The El Nino fails to amplify. Peak SST values ~2 deg

50OE 100OE 150OE 160OW 110OW 60OW 10OW

Longitude

JUN

JUL

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Plot resolution is 1.4063 in x and 120 in yTime-longitude plot at 0.00 deg NX-Surface stress contoured every 0.02 N/m2HOPE gcm: 0001

Interpolated in y19960501 + 91 days

difference from19970501 + 91 days

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MAGICS 6.11 verhandi - neh Tue Nov 4 14:28:36 2008

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Longitude

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No interpolation19960501 + 91 days


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Plot resolution is 1.4063 in x and 24 in yTime-longitude plot at 0.00 deg N and 5.0 metres depthPotential temperature contoured every 0.5 deg CHOPE gcm: 0001

No interpolation19960501 + 91 days


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New Atmospheric Cycles:

S3 in red

Changes in the parameterization of deep convection improve the representation of the MJO and he Onset of El Nino 1997.

But this is not all the story, some other aspects get worse: too strong easterlies in the Eastern Pacific.


New Atmos cycles (33r1 versus S3)

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NINO4 mean absolute SST

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NINO4 mean SST drift

Fcast S3 Fcast f05p

MAGICS 6.12n verhandi - neh Tue Jul 21 14:30:33 2009


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NINO4 SST anomaly correlation


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NINO4 SST rms errors

Fcast S3 Fcast f05p Persistence Ensemble sd


33r1

S3

•The new cycles have colder bias (resulting from stronger easterlies)

•There is too much variability in the Western Pacific, larger RMS error

•The anomaly correlation is also degraded.


NEMO-CONTROL

NEMO-ASSIM

HOPE-ASSIM

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Fcast f4wj Fcast f6ji Fcast f05p

MAGICS 6.12n verhandi - neh Wed May 20 11:55:34 2009

•NEMO has colder bias than HOPE in the Pacific, and larger variability => larger RMS error

•NEMOVAR has impact on the drift

•NEMOVAR improves correlation 0 1 2 3 4 5 6 7Forecast time (months)

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NINO4 034a anomaly correlation


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NINO4 034a rms errors

Fcast f4wj Fcast f6ji Fcast f05p Persistence Ensemble sd


Impact of ocean model NEMO/NEMOVAR



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EQATL 034a anomaly correlation


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EQATL 034a rms errors

Fcast f4wj Fcast f6ji Fcast f05p Persistence Ensemble sd


Impact on Equatorial Atlantic

NEMOVAR improves correlation in the Atlantic.

Better than NEMO-CONTROL and HOPE-ASSIM

It is the first time that Assimilation has a positive impact on the Atlantic Skill !!

NEMO-CONTROL

NEMO-ASSIM

HOPE-ASSIM


• Seasonal integrations for winters 1990/91-2005/06

• Atmosphere only with prescribed SST/sea ice

• Resolutions: TL159, TL255 and TL511 (all 91 levels in the vertical)

Impact of Atmospheric Resolution

Courtesy of Thomas Joung

Experimental Setup


Mean Precipitation

Precipitation GPCP (12-3 1990-2005)

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Precipitation f127-GPCP (12-3 1990-2005)

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Precipitation GPCP (12-3 1990-2005)

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Precipitation f0cm-GPCP (12-3 1990-2005)

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Total Precipitation f127 (12-3 1990-2005)

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Total Precipitation f0cm-f127 (12-3 1990-2005)

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GPCP

T159-GPCP

T511-GPCP

T511-T159

Courtesy of Thomas Joung


Winds @ 850 hPa

20.0m/sMean Wind 850hPa er40 (12-3 1990-2005)

5.0m/sWind Difference 850hPa f127-er40 (12-3 1990-2005)20.0m/sMean Wind 850hPa er40 (12-3 1990-2005)

5.0m/sWind Difference 850hPa f0cm-er40 (12-3 1990-2005)

20.0m/sMean Wind 850hPa er40 (12-3 1990-2005)

5.0m/sWind Difference 850hPa f3oa-er40 (12-3 1990-2005)T159-ERA40 T255-ERA40

T511-ERA40

•Increasing horizontal resolution beneficial for surface winds in the tropical Pacific.

•TL255 might be enough to reduce the surface wind bias


EUROBRISA: some ideas

• Paper on La Nina effects on SA

• Work of optimality of historical hindcasts record. Also useful for decadal forecasting.

• Can EUROBRISA detect the impact of the ocean observing system?.

• How to get rid of the “negative” correlation in the calibrated product. See next slide.

1) less weight to the statistical 2) linear combination of “uncalibrated” + calibrated. [Penalty to

account for the error in the calibration coefficients]

• Work on temporal properties of the predictability: show when the calibration works best.


La Nina 2007-2008

2006 2007 2008 2009

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Ensemble sizes are 40 (0001), 40 (0001) and 40 (0001) SST obs: NCEP OIv2ECMWF forecasts at month 5

NINO3.4 SST forecast anomalies

Obs. anom. ECMWF S3 Met Office S3 Météo-France S3


The EUROSIP multimodel captured well the onset, amplitude and long duration of La Nina conditions.

No individual model capture it correctely.

How was the impact in precip over South America?

How did EUROBRISA performed?

Scope for a paper?

Others: in Nino3 there were some misses.

The calibrated plumes are slightly better: not misses, reduced spread


2006 2007 2008 2009-4

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Ensemble sizes are 33 (MM ) and 33 (MMB ) SST obs: NCEP OIv2ECMWF forecasts at month 5

NINO3 SST forecast anomalies

Obs. anom. EUROSIP10 EUROSIP11


Multimodel: Raw versus calibrated

Raw

Bayesian


Curiosity: calibration versus multi-model

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Ensemble sizes are 33 (MMB ) and 11 (0001)252 start dates from 19870101 to 20071201

NINO4 SST rms errors

EUROSIP10 C000 ecm1 C001 Persistence Ensemble sd


EUROSIP

ECWMF only (calibrated)

For ENSO, ECMWF calibrated can beat the multi-model.

This is not the case for the Atlantic.

The calibration of the ECMWF is only a scaling factor of the interannual variance

(as to match the observed variability)


Problems with Negative correlation

ECMWF UKMO CPTEC Meteo France

EMPIRICAL CALIBRATED

•None of the GCMs show large areas nor values of –ve correlation

•The empirical shows large areas of –ve correlation

•The calibrated shows large areas and large values of –ve correlation

Why? No robust calibration?


Temporal properties of Predictability

2m Temperature Amazones

!"##$% %$#&'( #& #&$#

& )#*#*"

!""#$ $# "%&' "% "%#"

% ()"*("*!)

Anomaly Correlation Temperature

Anomaly Correlation Precipitation

Predictability as a function of target month

Predictability as a function of initial conditions


How the time-properties change in EUROBRISA?

Anomaly Correlation Temperature

Anomaly Correlation Precipitation

!"##$% %$#&'( #& #&$#

& )#*#*"

!""#$ $# "%&' "% "%#"

% ()"*("*!

North-East

Brasil

Target month is more predictable

Feb/March as a Window of

predictability


Summary

• Evidence that ENSO forecast is improving in time. Evidence of improved initialization.

Can the improvements be seen in the EUROBRISA system? Need to assess the value of long hindcast calibrating records.

• The ECMWF S4 will be ready in 2010. It will be based on NEMOVAR It will use ERA-Interim from 1989-onwards. Improvement in the Equatorial

Atlantic Probably improved Intraseasonal variability. It is early times to assess performace

• How to go on with EUROBRISA Outstanding issue of no robust calibration (-ve skill in x-validation) Explore/exploit the temporal features of prediction skill Document performance of La Nina 2007-2008?

Documents

Ongoing developments at ECMWF