My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 1

Delayed Ocean Analysis ~12 days

Real Time Ocean Analysis ~Real time

ECMWF:Weather and Climate Dynamical Forecasts

ECMWF:Weather and Climate Dynamical Forecasts

Medium-Range (10-day)Partial coupling

Medium-Range (10-day)Partial coupling

Seasonal ForecastsFully coupled

Seasonal ForecastsFully coupled

Extended + Monthly Fully coupled

Extended + Monthly Fully coupled

Ocean model

Atmospheric model

Wave model

Atmospheric model

Ocean model

Wave model

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 2

• Observations

� Quality controlled: (bias correction, black listing) retrospective and real time

� Profiles of T/S, SL from Altimeter, MDT, SST, sea-ice concentration-thickness, possibly ocean colour (climatology, maps)

• Ocean re-analysis:

� Initialization, verification, calibration of seasonal (monthly, decadal) forecasts

� Long and Consistent records

� Estimates of uncertainty

� Timely

� Compatibility with ocean model in forecasting system

• Software sharing & RD development

� Ocean Model (example NEMO). Software and configurations

o High horizontal/vertical resolution needed. “Off MyOcean Shelf “

� Data assimilation

• Outlook:

� explicit/improved representation of air-sea interaction for all forecast ranges and data assimilation

Seasonal Needs: Outline & Summary

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 3

End-To-End Seasonal forecasting System

EN

SE

MB

LE

GE

NE

RA

TIO

N

COUPLED MODEL Tailored Forecast

PRODUCTS

Initialization Forward Integration Forecast Calibration

OCEAN

PR

OB

AB

ILIS

TIC

CA

LIB

RA

TE

D F

OR

EC

AST

JUL2006

AUG SEP OCT NOV DEC JAN2007

FEB MAR APR MAY JUN JUL AUG SEP

-1

0

1

2

Ano

mal

y (d

eg C

)

-1

0

1

2

Monthly mean anomalies relative to NCEP adjusted OIv2 1971-2000 climatologyECMWF forecast from 1 Jan 2007

NINO3.4 SST anomaly plume

Produced from real-time forecast data

System 380°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

14.3 10.39.8 13.325.1 26.22 2.9

No Significance 90% Significance 95% Significance 99% Significance

FORECAST CLIMATE

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 4

0 1 2 3 4 5 6Forecast time (months)

0

0.2

0.4

0.6

0.8

1

1.2

Rm

s er

ror (

deg

C)

Ensemble sizes are 5 (0001), 5 (0001) and 5 (0001) 64 start dates from 19870401 to 20021201

NINO3 SST rms errors

Fcast S3 Fcast S2 Fcast S1 Persistence Ensemble sd

•Steady progress: ~1 month/decade skill gain

•How much is due to the initialization, how much to model development?

S1 S2 S3

TOTAL GAIN

OC INI

MODEL

0

5

10

15

20

25

30

35

40

1%

Relative Reduction in SST Forecast ErrorECMWF Seasonal Forecasting Systems

TOTAL GAIN OC INI MODEL

Half of the gain on forecast skill is due to improved ocean initialization

A decade of progress on ENSO prediction

Balmaseda et al 2010

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 5

Dealing with model error: Hindcasts

Ocean

reanalysis

Coupled Hindcasts, needed to estimate climatological PDF,

require a historical ocean reanalysis

Real time Probabilistic

Coupled Forecast

time

Consistency between historical and real-time initial conditions is required.

Hindcasts are also needed for skill estimation

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 6

Real Time Ocean Observations

ARGO floats XBT (eXpandable BathiThermograph)

Moorings

Satellite

SST

Sea Level

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 7

Skill: Ocean observations and Winds

Increase (%) in MAE of SST forecasts from removing external information

(1-7 months)

-10

-5

0

5

10

15

20

25

30

NIN

O3

NIN

O3.

4

NIN

O4

TR

PA

C

EQ

IND

IND

1

IND

2

NS

TR

AT

L

EQ

AT

L

%

OC DATA

WINDS

DATA+WINDS

•Both Wind and ocean observations contribute to the initialization

•No Observing system is redundant

•The winds from ERA-Interim produce better forecast over the Equatorial Atlantic.

ERA40+OPS

ERA-INTERIM

EQATL SST Forecasts correlation

1989-2006

Balmaseda et al 2010

Dee et al 2011

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 8

Producing Reliable Forecasts: Calibration

Persistence

ECMWF

ensemble spread

RMS error of Nino3 SST anomalies

Bayesian Calibration

EUROSIP

Usually, the forecast systems are not reliable: RMS > Spread

Can we reduce the error? Or Can we increase the spread?

Multimodel

Calibration. Long records of initial conditions are needed for calibration

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 9

Which SST product to use?

• OIV2_025_AVHRR: bias cold in the global mean (regional differences) respect cmip5-proto

•Bias decreases with time. Weaker interannual variability

•Fit to insitu Temperature: bias cold in tropics, better in mid latitudes.

• Clear need for GLOBAL, CONSISTENT, UNBIASED, LONG records of HIGH SPATIAL and TEMPORAL RESOLUTION (1/4 of Degree, daily)

• Consistent and GLOBAL SEA-ICE concentrationOSTIA SST from 2008, similar to oiv2_025_AVHR_AMSR

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 10

Impact of NEMOVAR on Seasonal Forecasts

Prototype of S4: latest NEMOVAR+36r4. Anomaly Correlation

0 1 2 3 4 5 6 7Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

Ano

mal

y co

rrel

atio

n

wrt NCEP adjusted OIv2 1971-2000 climatologyEQ2 034a anomaly correlation

0 1 2 3 4 5 6 7Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

Ano

mal

y co

rrel

atio

n

wrt NCEP adjusted OIv2 1971-2000 climatologyATL3 034a anomaly correlation

0 1 2 3 4 5 6 7Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

Ano

mal

y co

rrel

atio

n

wrt NCEP adjusted OIv2 1971-2000 climatologyEQIND 034a anomaly correlation

0 1 2 3 4 5 6 7Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

An

oma

ly c

orr

elat

ion

wrt NCEP adjusted OIv2 1971-2000 climatology

NSTRATL 034a anomaly correlation

0 1 2 3 4 5 6 7Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

Ano

mal

y co

rrel

atio

n

wrt NCEP adjusted OIv2 1971-2000 climatologyNSTRPAC 034a anomaly correlation

0 1 2 3 4 5 6 7Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

Ano

mal

y co

rrel

atio

n

wrt NCEP adjusted OIv2 1971-2000 climatologySSTRATL 034a anomaly correlation

NEMOVAR NEMO-NoObs

Software products: ECMWF uses NEMO (ORCA1 configuration)

ECMWF uses NEMOVAR (collaborative project)

CENTRAL EQ. PACIFIC CENTRAL EQ. ATLANTICEQ. INDIAN

N SubTrop PACIFIC N SubTrop ATLANTIC S SubTrop ATLANTIC

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 11

Time scales for ocean-atmosphere interaction

ATM delay:

days-weeks

OCN delay:

Hours-days-decades

ATM forcing

OCN response

OCN forcing

ATM response

days weeks Months/years Decades and beyond

Tropical cyclones

Surface waves

Diurnal Cycle

Madden-Julian Oscillation

Tropical Instability Waves

Equatorial Ocean Dynamics:

ENSO, IOD

Seasonal ML variations: NAO?

Subtropical Gyre, Rossby Waves, THC, MOC

Pacific/ Atlantic Decadal Variability

Boundary layer processes

Heating/cooling

Evaporation/precip

Momentum transfer

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 12

Monthly Forecasts needs

• Madden Julian Oscillation (MJO) is corner stone for monthly forecasting (as ENSO is for seasonal)

• It influences NAO regimes (Cassau et al 2008) and predictability over Europe (Vitart)

• MJO forecasts needs interactive ocean, good representation of ocean mixing (high vertical

resolution)

Woolnough et al, MWR 2007

Anomaly Correlation

Persisted SST anomalies

OGCM (10 m vertical res)

Mixed layer (1 m vertical res)

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 13

O-A interaction over SST fronts

Air-Sea Interaction also occurs at small scales, such as that of the Western Boundary currents (above) and Tropical Instability Waves TIW (left). The small scales are set up by the ocean

Need of high resolution ocean models

Minobe et al, Nature, 2008

Chelton and Song

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 14

Winds, Waves, Currents

Bidlot, 2010

Wind

Neutral Wind ~stress

Wave Height

Wind, Waves and ocean currents need to be treated in coupled mode

Additionally, the effect of waves on ocean mixing should be considered.

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 15

Saha et al BAMS 2010

Tropical Precipitation and SST relationship

OBS

CFSR

NCEP R1

NCEP R2

Atmospheric models forced by observed SST do not capture the tropical SST/precip lag relationship

This problem was also evident int the uncoupled NCEP atmospheric re-analysis.

The new CFSR is an improvement in this respect.

My Ocean User Meeting, Stockholm, 7-8 April 2011 - Seasonal Forecasting - 16

• Observations

� Quality controlled: (bias correction, black listing) retrospective and real time

� Profiles of T/S, SL from Altimeter, MDT, SST, sea-ice concentration-thickness, possibly ocean colour (climatology, maps)

• Ocean re-analysis:

� Initialization, verification, calibration of seasonal (monthly, decadal) forecasts

� Long and Consistent records

� Estimates of uncertainty

� Timely

� Compatibility with ocean model in forecasting system

• Software sharing & RD development

� Ocean Model (example NEMO). Software and configurations

o High horizontal/vertical resolution needed. “Off MyOcean Shelf “

� Data assimilation

• Outlook:

� explicit/improved representation of air-sea interaction for all forecast ranges and data assimilation

Seasonal Needs: Outline & Summary