ECMWF: Weather and Climate Dynamical Forecasts
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- Archibald Holmes
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1 ECMWF: Weather and Climate Dynamical Forecasts Medium-Range (0-day) Partial coupling Extended + Monthly Fully coupled Seasonal Forecasts Fully coupled Atmospheric model Atmospheric model Wave model Wave model Ocean model Real Time Ocean Analysis ~Real time Ocean model Delayed Ocean Analysis ~2 days My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting -
2 Seasonal Needs: Outline & Summary 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 My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 2
3 End-To-End Seasonal forecasting System COUPLED MODEL Tailored Forecast PRODUCTS OCEAN ENSEMBLE GENERATION PROBABILISTIC CALIBRATED FORECAST 80 N 60 N 40 N 0 20 S 40 S 40 S NINO3.4 SST anomaly plume 50 S 50 S ECMWF forecast from Jan S 60 S Monthly mean anomalies relative to NCEP adjusted OIv climatology 70 S 70 S 80 S 20 E 70 N 70 N 50 N 50 N 30 N 30 N 20 N 20 N N 0 N 0 S 0 S 30 S 30 S 20 E 40 E FORECAST 40 E 60 E 60 E 80 E 2 Anomaly (deg C) 80 E 00 E 0 - JUL E 20 E 20 E 40 E System 3 40 E 60 E CLIMATE 60 E W AUG SEP OCT NOV DEC JAN W 40 W 40 W 20 W 20 W 00 W 00 W 80 W 80 W 60 W 60 W 40 W No Significance 90% Significance 95% Significance 99% Significance 40 W 20 W 20 W FEB MAR APR MAY JUN JUL AUG SEP N 60 N 40 N 0 20 S 80 S Produced from real-time forecast data Initialization Forward Integration Forecast Calibration My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 3
4 A decade of progress on ENSO prediction.2 NINO3 SST rms errors 64 start dates from to Ensemble sizes are 5 (000), 5 (000) and 5 (000) Fcast S3 Fcast S2 Fcast S Persistence Ensemble sd Relative Reduction in SST Forecast Error ECMWF Seasonal Forecasting Systems TOTAL GAIN Rms error (deg C) % OC INI MODEL Forecast time (months) 0 S S2 S3 5 Steady progress: ~ month/decade skill gain How much is due to the initialization, how much to model development? 0 TOTAL GAIN OC INI MODEL Half of the gain on forecast skill is due to improved ocean initialization My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - Balmaseda et al 200 4
5 Dealing with model error: Hindcasts time Ocean reanalysis Real time Probabilistic Coupled Forecast Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis 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 20 - Seasonal Forecasting - 5
6 Real Time Ocean Observations Moorings ARGO floats XBT (expandable BathiThermograph) Satellite SST Sea Level My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 6
7 Skill: Ocean observations and Winds Increase (%) in MAE of SST forecasts from removing external information (-7 months) 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. % OC DATA WINDS DATA+WINDS EQATL SST Forecasts correlation NINO3 NINO3.4 NINO4 TRPAC EQIND IND IND2 NSTRATL EQATL ERA40+OPS ERA-INTERIM Balmaseda et al 200 Dee et al 20 My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 7
8 Producing Reliable Forecasts: Calibration 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 RMS error of Nino3 SST anomalies Persistence ECMWF EUROSIP Bayesian Calibration ensemble spread My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 8
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 (/4 of Degree, daily) OSTIA SST from 2008, similar My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - to oiv2_025_avhr_amsr 9 Consistent and GLOBAL SEA-ICE concentration
10 Anomaly correlation Impact of NEMOVAR on Seasonal Forecasts Prototype of S4: latest NEMOVAR+36r4. Anomaly Correlation CENTRAL EQ2 034a EQ. anomaly PACIFIC correlation wrt NCEP adjusted OIv climatology Forecast time (months) Anomaly correlation CENTRAL ATL3 034a EQ. anomaly ATLANTIC correlation wrt NCEP adjusted OIv climatology Forecast time (months) NEMOVAR Anomaly correlation NEMO-NoObs EQ. EQIND INDIAN 034a anomaly correlation wrt NCEP adjusted OIv climatology Forecast time (months) N NSTRPAC SubTrop 034a PACIFIC anomaly correlation N NSTRATL SubTrop 034a ATLANTIC anomaly correlation S SSTRATL SubTrop 034a ATLANTIC anomaly correlation wrt NCEP adjusted OIv climatology wrt NCEP adjusted OIv climatology wrt NCEP adjusted OIv climatology Anomaly correlation Anomaly correlation Anomaly correlation Forecast time (months) Forecast time (months) Forecast time (months) Software products: ECMWF uses NEMO (ORCA configuration) ECMWF uses NEMOVAR (collaborative project) My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 0
11 Time scales for ocean-atmosphere interaction ATM delay: days-weeks ATM response ATM forcing OCN forcing OCN response Heating/cooling Evaporation/precip Momentum transfer OCN delay: Hours-days-decades days weeks Months/years Decades and beyond Tropical cyclones Surface waves Diurnal Cycle Boundary layer processes 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 My Ocean User Meeting, Stockholm, 7-8 April 20 - 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) Anomaly Correlation Persisted SST anomalies OGCM (0 m vertical res) Mixed layer ( m vertical res) Woolnough et al, MWR 2007 My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 2
13 O-A interaction over SST fronts Minobe et al, Nature, 2008 Chelton and Song 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 My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 3
14 Winds, Waves, Currents Wind Neutral Wind ~stress Wind, Waves and ocean currents need to be treated in coupled mode Additionally, the effect of waves on ocean mixing should be considered. Wave Height Bidlot, 200 My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 4
15 Tropical Precipitation and SST relationship Atmospheric models forced by observed SST do not capture the tropical SST/precip lag relationship OBS CFSR NCEP R NCEP R2 This problem was also evident int the uncoupled NCEP atmospheric reanalysis. The new CFSR is an improvement in this respect. Saha et al BAMS 200 My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 5
16 Seasonal Needs: Outline & Summary 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 My Ocean User Meeting, Stockholm, 7-8 April 20 - Seasonal Forecasting - 6
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