My Ocean WP18, Athens 29-30 September 2009 1 Medium Range Weather/ Monthly and Seasonal forecasts Magdalena A. Balmaseda ECMWF (UK) Climate & Seasonal

My Ocean WP18 , Athens 29-30 September 2009 1

Medium Range Weather/

Monthly and Seasonal forecasts

Magdalena A. Balmaseda ECMWF (UK)

Climate & Seasonal Forecasts


WP18: Task on Climate Seasonal Forecast

• Participants: Met Office Meteo France CMCC ECMWF MetNo (?)

• First suggestion: generalize a and split the task to

1. Seamless weather to seasonal forecasting2. Climate Impacts


Overview

• Forecasts at different time scales: SEAMLESS prediction

• Seasonal Forecasts needs Why do we want forecast at seasonal time scales? End To End Seasonal Forecasting Systems Initialization Calibration

• Monthly Forecasts needs

• Weather Forecasts needs

• Climate Reanalysis (atmosphere/ocean/ice) needs


• There is a clear demand for reliable seasonal forecasts: Forecasts of anomalous rainfall and temperature at 3-6 months ahead

• For a range of societal, governmental, economic applications:

Agriculture Heath (malaria, dengue,…) Energy management Markets, insurance Water resource management,

• Huge progress in the last decade: Operational seasonal forecasts in several centres Pilot/Research progress for demonstrating applicability

(DEMETER,IRI,EUROBRISA,…) Build-up of community infrastructure (at WMO level)


The basis for extended range forecasts

•Forcing by boundary conditions changes the atmospheric circulation, modifying the large scale patterns of temperature and rainfall, so that the probability of occurrence of certain events deviates significantly from climatology.

Important to bear in mind the probabilistic nature of climate forecasts

How long in advance?: from seasons to decades

The possibility of seasonal forecasting has clearly been demonstrated

Decadal forecasting activities are now starting.

•The boundary conditions have longer memory, thus contributing to the predictability. Important boundary forcing:

SST: ENSO, Indian Ocean Dipole, Atlantic SST Land: snow depth, soil moisture Atmospheric composition: green house gases, aerosols,… Ice?


End-To-End Seasonal forecasting System

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COUPLED MODEL Forecast PRODUCTS

Initialization Forward Integration Forecast Calibration

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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

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20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

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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

Ensemble size = 40,climate size = 70Forecast start reference is 01/06/2005Tropical Storm FrequencyECMWF Seasonal Forecast

Significance level is 90%JASON

FORECAST CLIMATE


Importance of Initialization

•Atmospheric point of view: Boundary condition problem Forcing by lower boundary conditions changes the atmospheric

circulation.“Loaded dice”

•Oceanic point of view: Initial value problem Prediction of tropical SST: need to initialize the ocean subsurface.

o Emphasis on the thermal structure of the upper oceano Predictability is due to higher heat capacity and predictable dynamics

A simple way: ocean model + surface fluxes.o But uncertainty in the fluxes is too large to constrain the solution.

Alternative : ocean model + surface fluxes + ocean observations o Using a data assimilation system. o The challenge is to initialize the thermal structure

– without disrupting the dynamical balances (wave propagation is important)– While preserving the water-mass characteristics


Real Time Ocean Observations

ARGO floatsXBT (eXpandable BathiThermograph)

Moorings

Satellite

SST

Sea Level


Coupled Hindcasts, needed to estimate model climatological PDF, require a historical ocean reanalysis

Real time Probabilistic Coupled Forecast

time

Ocean reanalysis

•Quality of reanalysis affects the climatological PDF, and therefore the real-time forecast

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

Re-forecasting needs an ocean re-analysis

Models have errors and direct model output needs calibration


Ocean data assimilation improves the forecast skill

(Alves et al 2003)

Data Assimilation

Data Assimilation

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Impact of Data Assimilation

Forecast Skill


A decade of progress on ENSO prediction

•Steady progress: ~1 month/decade skill gain

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

S1 S2 S3

Relative Reduction in SST Forecast ErrorECMWF Seasonal Forecasting Systems

TOTAL GAIN

OC INI

MODEL

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%TOTAL GAIN OC INI MODEL

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

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

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wrt NCEP adjusted OIv2 1971-2000 climatology

NINO3.4 SST anomaly correlation

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

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

NINO3.4 SST rms errors

Fcast S3 Fcast S2 Fcast S1 Persistence

MAGICS 6.11 windmill - neh Mon Sep 14 11:48:01 2009


Ocean Observation & Reliable forecast products

Calibration and multi-model can increase the skill and reliability of forecasts.

In a general case, even the multi-model needs calibration.

Long records are needed for robust calibration and downscaling

RMS Error Ensemble Spread

A. Can we reduce the error? How much? (Predictability limit)

B. Can we increase the spread by improving the ensemble generation and calibration?

Forecast Systems are generally not reliable (RMS > Spread)


Requirements for Seasonal

• Data for initialization In situ QC data (historical and real time) SST, high resolution (historical and real time) Sea Level Anomalies (historical and real time) MDT Bottom pressure (?) Sea ice (in the future)

• Independent data for validation/ comparison Sea level data (gauges) Ocean currents (blended product) Ocean reanalysis from other centres (including those without model)

• Direct 3D ocean states: depending on centre Met-Office, INGV (OK), since they produce MyOcean reanalysis with their

own systems Meteo-France will try the ORCA2 product from Mercator ECMWF needs ORCA1 resolution

o Can not use the ¼ degree product directly

The longer the record, the better


ECMWF and 3D ocean reanalysis from My Ocean

• The time scales of the project precludes the direct output of the ¼ ocean re-analysis for seasonal forecasts

Quite expensive assessment.

• Alternative Interpolate ¼ into 1 deg Use the interpolated data. Directly: not very interesting experiment (?) Anomaly initialization.

• Other alternatives: Use My Ocean re-analysis of sea ice.

Consider other time scales (weather /monthly forecasting).

Start work on the implementation of the ¼ of degree model from MyOcean to be ready for future use of My Ocean products.


Delayed Ocean Analysis ~12 days

Real Time Ocean Analysis ~Real time

ECMWF:

Weather and Climate Dynamical Forecasts

ECMWF:

Weather and Climate Dynamical Forecasts

10-Day Medium-Range

Forecasts

10-Day Medium-Range

Forecasts

Seasonal Forecasts

Seasonal Forecasts

Monthly Forecasts

Monthly Forecasts

Ocean model

Atmospheric model

Wave model

Atmospheric model

Ocean model

Wave model


Monthly forecasts

• Mixed layer processes are important

• Currently, monthly forecasts use the same ocean model configuration that seasonal

• Ideally, one would use a dynamical ocean model with Higher horizontal/vertical resolution

• Alternative, an ocean mixed layer model can be used: High horizontal/vertical resolution but not dynamics High resolution initial conditions for this ML model will be needed


Requirements for Monthly Forecast

• High resolution 3D ocean re-analysis to initialize ML model Temperature and Salinity. Velocities Long Reanalysis and timely real-timi (within 12-24 h)

• Possibility 1: Get data for a long term reanalysis. Exact period to be discussed. (1993-2005) is a

possibility) Use it to initialize the ML model and conduct a series of monthly hindcasts. Assess skill respect initial conditions from a low resolution ocean model. Problem: human resources are scarce

• Possibility 2: Start work on the implementation of the ¼ of degree model from

MyOcean to be ready for future use of My Ocean products.


Medium Range Weather forecasts

• They will benefit from better representation of air-sea interaction:

Currents coupled to the wave model High resolution SST with diurnal cycle. Sea-ice

• Currently, persisted SST anomalies are used

• Interest in testing coupling to currents. Or at least, initialization of currents

And use persisted currents during the forecast

• Ideally, one would use a dynamical ocean/sea-ice model with

Higher horizontal resolution Higher vertical resolution But software does not exist yet

• Alternative, ML model as in monthly.


Requirements for Medium Range

• Possibility 1: Use ocean currents. It involves other groups at ECMWF. Need for skill assessment of the product.

• Possibility 2: Start work on the implementation of the ¼ of degree

model from MyOcean to be ready for future use of My Ocean products.


Summary

• Extend the name of the task to seamless prediction, to include

medium range and seasonal. Separate task on climate impacts.

• Direct products from TACS will be used by the different partners.

Ready to provide assessment for different time scales

• URD From ECMWF can include Atmospheric Re-analysis, Medium Range

weather forecast, monthly and seasonal

• Use of 3D reanalysis Not prescriptive. Depending of group. Assessment of seasonal from CMCC, MetOffice and Meteo-France ECMWF may not provide assessment of 3D products for seasonal. It could

(needs further discussion/iterations. Resource dependent)o Use Sea-ice in seasonal o Initialization of ML model for monthly (resources)o Use of velocity data for the wave model in medium range.o Start implementing MyOcean software (1/4 degree ocean model/sea ice)


THE END

Documents

My Ocean WP18, Athens 29-30 September 2009 1 Medium Range Weather/ Monthly and Seasonal forecasts Magdalena A. Balmaseda ECMWF (UK) Climate & Seasonal